├── scripts └── evaluation │ ├── evaluate_coverage.sh │ └── evaluate_precision.sh ├── CONTRIBUTING.md ├── CODE_OF_CONDUCT.md ├── results ├── coverage │ ├── gpt-3.out │ ├── gpt-4.out │ ├── gpt-3.5.out │ ├── m-nta.gpt-3.out │ ├── m-nta.gpt-4.out │ └── m-nta.gpt-3.5.out └── precision │ ├── gpt-3.out │ ├── gpt-4.out │ ├── gpt-3.5.out │ ├── m-nta.gpt-4.out │ ├── m-nta.gpt-3.out │ └── m-nta.gpt-3.5.out ├── src └── evaluation │ ├── evaluate_precision.py │ └── evaluate_coverage.py ├── LICENSE.txt ├── README.md ├── LICENSE_Wikidata.txt └── data └── names └── gpt └── ja.gpt-4.json /scripts/evaluation/evaluate_coverage.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | SCORE_THRESHOLD=2 4 | SYSTEM=m-nta 5 | PREDICTIONS_DIR=data/names/m-nta/with_gpt-3.5 6 | REFERENCES_DIR=data/names/gold 7 | LOCALES=( ar de en es fr it ja ko ru zh ) 8 | 9 | for locale in ${LOCALES[@]}; do 10 | if [[ ! -f ${PREDICTIONS_DIR}/${locale}.${SYSTEM}.json ]]; then 11 | continue 12 | fi 13 | 14 | python src/evaluation/evaluate_coverage.py \ 15 | --references_path ${REFERENCES_DIR}/${locale}.json \ 16 | --predictions_path ${PREDICTIONS_DIR}/${locale}.${SYSTEM}.json \ 17 | --score_threshold ${SCORE_THRESHOLD} 18 | done -------------------------------------------------------------------------------- /scripts/evaluation/evaluate_precision.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | SCORE_THRESHOLD=1 4 | SYSTEM=m-nta 5 | PREDICTIONS_DIR=data/names/m-nta/with_gpt-3.5 6 | REFERENCES_DIR=data/names/gold 7 | LOCALES=( ar de en es fr it ja ko ru zh ) 8 | 9 | for locale in ${LOCALES[@]}; do 10 | if [[ ! -f ${PREDICTIONS_DIR}/${locale}.${SYSTEM}.json ]]; then 11 | continue 12 | fi 13 | 14 | python src/evaluation/evaluate_precision.py \ 15 | --references_path ${REFERENCES_DIR}/${locale}.json \ 16 | --predictions_path ${PREDICTIONS_DIR}/${locale}.${SYSTEM}.json \ 17 | --score_threshold ${SCORE_THRESHOLD} 18 | done -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contribution Guide 2 | 3 | Thanks for your interest in contributing. This project was released to accompany a research paper for purposes of reproducibility, and beyond its publication there are limited plans for future development of the repository. 4 | 5 | While we welcome new pull requests and issues please note that our response may be limited. Forks and out-of-tree improvements are strongly encouraged. 6 | 7 | ## Before you get started 8 | 9 | By submitting a pull request, you represent that you have the right to license your contribution to Apple and the community, and agree by submitting the patch that your contributions are licensed under the [LICENSE](LICENSE). 10 | 11 | We ask that all community members read and observe our [Code of Conduct](CODE_OF_CONDUCT.md). 12 | -------------------------------------------------------------------------------- /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | # Code of Conduct 2 | 3 | ## Our Pledge 4 | 5 | In the interest of fostering an open and welcoming environment, we as 6 | contributors and maintainers pledge to making participation in our project and 7 | our community a harassment-free experience for everyone, regardless of age, body 8 | size, disability, ethnicity, sex characteristics, gender identity and expression, 9 | level of experience, education, socio-economic status, nationality, personal 10 | appearance, race, religion, or sexual identity and orientation. 11 | 12 | ## Our Standards 13 | 14 | Examples of behavior that contributes to creating a positive environment 15 | include: 16 | 17 | * Using welcoming and inclusive language 18 | * Being respectful of differing viewpoints and experiences 19 | * Gracefully accepting constructive criticism 20 | * Focusing on what is best for the community 21 | * Showing empathy towards other community members 22 | 23 | Examples of unacceptable behavior by participants include: 24 | 25 | * The use of sexualized language or imagery and unwelcome sexual attention or 26 | advances 27 | * Trolling, insulting/derogatory comments, and personal or political attacks 28 | * Public or private harassment 29 | * Publishing others' private information, such as a physical or electronic 30 | address, without explicit permission 31 | * Other conduct which could reasonably be considered inappropriate in a 32 | professional setting 33 | 34 | ## Our Responsibilities 35 | 36 | Project maintainers are responsible for clarifying the standards of acceptable 37 | behavior and are expected to take appropriate and fair corrective action in 38 | response to any instances of unacceptable behavior. 39 | 40 | Project maintainers have the right and responsibility to remove, edit, or 41 | reject comments, commits, code, wiki edits, issues, and other contributions 42 | that are not aligned to this Code of Conduct, or to ban temporarily or 43 | permanently any contributor for other behaviors that they deem inappropriate, 44 | threatening, offensive, or harmful. 45 | 46 | ## Scope 47 | 48 | This Code of Conduct applies within all project spaces, and it also applies when 49 | an individual is representing the project or its community in public spaces. 50 | Examples of representing a project or community include using an official 51 | project e-mail address, posting via an official social media account, or acting 52 | as an appointed representative at an online or offline event. Representation of 53 | a project may be further defined and clarified by project maintainers. 54 | 55 | ## Enforcement 56 | 57 | Instances of abusive, harassing, or otherwise unacceptable behavior may be 58 | reported by contacting the open source team at [opensource-conduct@group.apple.com](mailto:opensource-conduct@group.apple.com). All 59 | complaints will be reviewed and investigated and will result in a response that 60 | is deemed necessary and appropriate to the circumstances. The project team is 61 | obligated to maintain confidentiality with regard to the reporter of an incident. 62 | Further details of specific enforcement policies may be posted separately. 63 | 64 | Project maintainers who do not follow or enforce the Code of Conduct in good 65 | faith may face temporary or permanent repercussions as determined by other 66 | members of the project's leadership. 67 | 68 | ## Attribution 69 | 70 | This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 1.4, 71 | available at [https://www.contributor-covenant.org/version/1/4/code-of-conduct.html](https://www.contributor-covenant.org/version/1/4/code-of-conduct.html) -------------------------------------------------------------------------------- /results/coverage/gpt-3.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/gpt/ar.gpt-3.json... 6 | # of entities in the references: 999 7 | # of entities in the predictions: 853 8 | --------------------------- 9 | Coverage - P = 24.3 10 | Coverage - R = 14.6 11 | Coverage - F1 = 18.2 12 | =========================== 13 | 14 | =========================== 15 | Starting evaluation... 16 | --------------------------- 17 | - Loading references from data/names/gold/de.json... 18 | - Loading predictions from data/names/gpt/de.gpt-3.json... 19 | # of entities in the references: 990 20 | # of entities in the predictions: 932 21 | --------------------------- 22 | Coverage - P = 52.4 23 | Coverage - R = 43.4 24 | Coverage - F1 = 47.4 25 | =========================== 26 | 27 | =========================== 28 | Starting evaluation... 29 | --------------------------- 30 | - Loading references from data/names/gold/es.json... 31 | - Loading predictions from data/names/gpt/es.gpt-3.json... 32 | # of entities in the references: 999 33 | # of entities in the predictions: 938 34 | --------------------------- 35 | Coverage - P = 56.3 36 | Coverage - R = 38.0 37 | Coverage - F1 = 45.3 38 | =========================== 39 | 40 | =========================== 41 | Starting evaluation... 42 | --------------------------- 43 | - Loading references from data/names/gold/fr.json... 44 | - Loading predictions from data/names/gpt/fr.gpt-3.json... 45 | # of entities in the references: 997 46 | # of entities in the predictions: 936 47 | --------------------------- 48 | Coverage - P = 49.4 49 | Coverage - R = 41.9 50 | Coverage - F1 = 45.4 51 | =========================== 52 | 53 | =========================== 54 | Starting evaluation... 55 | --------------------------- 56 | - Loading references from data/names/gold/it.json... 57 | - Loading predictions from data/names/gpt/it.gpt-3.json... 58 | # of entities in the references: 995 59 | # of entities in the predictions: 937 60 | --------------------------- 61 | Coverage - P = 52.8 62 | Coverage - R = 46.4 63 | Coverage - F1 = 49.4 64 | =========================== 65 | 66 | =========================== 67 | Starting evaluation... 68 | --------------------------- 69 | - Loading references from data/names/gold/ja.json... 70 | - Loading predictions from data/names/gpt/ja.gpt-3.json... 71 | # of entities in the references: 997 72 | # of entities in the predictions: 814 73 | --------------------------- 74 | Coverage - P = 23.6 75 | Coverage - R = 19.5 76 | Coverage - F1 = 21.4 77 | =========================== 78 | 79 | =========================== 80 | Starting evaluation... 81 | --------------------------- 82 | - Loading references from data/names/gold/ko.json... 83 | - Loading predictions from data/names/gpt/ko.gpt-3.json... 84 | # of entities in the references: 993 85 | # of entities in the predictions: 851 86 | --------------------------- 87 | Coverage - P = 32.0 88 | Coverage - R = 21.9 89 | Coverage - F1 = 26.0 90 | =========================== 91 | 92 | =========================== 93 | Starting evaluation... 94 | --------------------------- 95 | - Loading references from data/names/gold/ru.json... 96 | - Loading predictions from data/names/gpt/ru.gpt-3.json... 97 | # of entities in the references: 999 98 | # of entities in the predictions: 903 99 | --------------------------- 100 | Coverage - P = 38.5 101 | Coverage - R = 27.9 102 | Coverage - F1 = 32.3 103 | =========================== 104 | 105 | =========================== 106 | Starting evaluation... 107 | --------------------------- 108 | - Loading references from data/names/gold/zh.json... 109 | - Loading predictions from data/names/gpt/zh.gpt-3.json... 110 | # of entities in the references: 974 111 | # of entities in the predictions: 883 112 | --------------------------- 113 | Coverage - P = 26.2 114 | Coverage - R = 19.1 115 | Coverage - F1 = 22.1 116 | =========================== 117 | 118 | -------------------------------------------------------------------------------- /results/coverage/gpt-4.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/gpt/ar.gpt-4.json... 6 | # of entities in the references: 999 7 | # of entities in the predictions: 853 8 | --------------------------- 9 | Coverage - P = 41.8 10 | Coverage - R = 23.2 11 | Coverage - F1 = 29.9 12 | =========================== 13 | 14 | =========================== 15 | Starting evaluation... 16 | --------------------------- 17 | - Loading references from data/names/gold/de.json... 18 | - Loading predictions from data/names/gpt/de.gpt-4.json... 19 | # of entities in the references: 990 20 | # of entities in the predictions: 932 21 | --------------------------- 22 | Coverage - P = 60.2 23 | Coverage - R = 44.7 24 | Coverage - F1 = 51.3 25 | =========================== 26 | 27 | =========================== 28 | Starting evaluation... 29 | --------------------------- 30 | - Loading references from data/names/gold/es.json... 31 | - Loading predictions from data/names/gpt/es.gpt-4.json... 32 | # of entities in the references: 999 33 | # of entities in the predictions: 938 34 | --------------------------- 35 | Coverage - P = 65.1 36 | Coverage - R = 41.6 37 | Coverage - F1 = 50.7 38 | =========================== 39 | 40 | =========================== 41 | Starting evaluation... 42 | --------------------------- 43 | - Loading references from data/names/gold/fr.json... 44 | - Loading predictions from data/names/gpt/fr.gpt-4.json... 45 | # of entities in the references: 997 46 | # of entities in the predictions: 936 47 | --------------------------- 48 | Coverage - P = 59.5 49 | Coverage - R = 45.3 50 | Coverage - F1 = 51.4 51 | =========================== 52 | 53 | =========================== 54 | Starting evaluation... 55 | --------------------------- 56 | - Loading references from data/names/gold/it.json... 57 | - Loading predictions from data/names/gpt/it.gpt-4.json... 58 | # of entities in the references: 995 59 | # of entities in the predictions: 937 60 | --------------------------- 61 | Coverage - P = 61.1 62 | Coverage - R = 49.6 63 | Coverage - F1 = 54.7 64 | =========================== 65 | 66 | =========================== 67 | Starting evaluation... 68 | --------------------------- 69 | - Loading references from data/names/gold/ja.json... 70 | - Loading predictions from data/names/gpt/ja.gpt-4.json... 71 | # of entities in the references: 997 72 | # of entities in the predictions: 814 73 | --------------------------- 74 | Coverage - P = 42.4 75 | Coverage - R = 27.9 76 | Coverage - F1 = 33.7 77 | =========================== 78 | 79 | =========================== 80 | Starting evaluation... 81 | --------------------------- 82 | - Loading references from data/names/gold/ko.json... 83 | - Loading predictions from data/names/gpt/ko.gpt-4.json... 84 | # of entities in the references: 993 85 | # of entities in the predictions: 851 86 | --------------------------- 87 | Coverage - P = 43.8 88 | Coverage - R = 28.6 89 | Coverage - F1 = 34.6 90 | =========================== 91 | 92 | =========================== 93 | Starting evaluation... 94 | --------------------------- 95 | - Loading references from data/names/gold/ru.json... 96 | - Loading predictions from data/names/gpt/ru.gpt-4.json... 97 | # of entities in the references: 999 98 | # of entities in the predictions: 903 99 | --------------------------- 100 | Coverage - P = 49.3 101 | Coverage - R = 33.9 102 | Coverage - F1 = 40.2 103 | =========================== 104 | 105 | =========================== 106 | Starting evaluation... 107 | --------------------------- 108 | - Loading references from data/names/gold/zh.json... 109 | - Loading predictions from data/names/gpt/zh.gpt-4.json... 110 | # of entities in the references: 974 111 | # of entities in the predictions: 883 112 | --------------------------- 113 | Coverage - P = 40.6 114 | Coverage - R = 25.5 115 | Coverage - F1 = 31.3 116 | =========================== 117 | 118 | -------------------------------------------------------------------------------- /results/coverage/gpt-3.5.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/gpt/ar.gpt-3.5.json... 6 | # of entities in the references: 999 7 | # of entities in the predictions: 853 8 | --------------------------- 9 | Coverage - P = 37.8 10 | Coverage - R = 21.5 11 | Coverage - F1 = 27.4 12 | =========================== 13 | 14 | =========================== 15 | Starting evaluation... 16 | --------------------------- 17 | - Loading references from data/names/gold/de.json... 18 | - Loading predictions from data/names/gpt/de.gpt-3.5.json... 19 | # of entities in the references: 990 20 | # of entities in the predictions: 932 21 | --------------------------- 22 | Coverage - P = 58.0 23 | Coverage - R = 44.8 24 | Coverage - F1 = 50.5 25 | =========================== 26 | 27 | =========================== 28 | Starting evaluation... 29 | --------------------------- 30 | - Loading references from data/names/gold/es.json... 31 | - Loading predictions from data/names/gpt/es.gpt-3.5.json... 32 | # of entities in the references: 999 33 | # of entities in the predictions: 938 34 | --------------------------- 35 | Coverage - P = 64.2 36 | Coverage - R = 41.8 37 | Coverage - F1 = 50.6 38 | =========================== 39 | 40 | =========================== 41 | Starting evaluation... 42 | --------------------------- 43 | - Loading references from data/names/gold/fr.json... 44 | - Loading predictions from data/names/gpt/fr.gpt-3.5.json... 45 | # of entities in the references: 997 46 | # of entities in the predictions: 936 47 | --------------------------- 48 | Coverage - P = 56.9 49 | Coverage - R = 45.3 50 | Coverage - F1 = 50.5 51 | =========================== 52 | 53 | =========================== 54 | Starting evaluation... 55 | --------------------------- 56 | - Loading references from data/names/gold/it.json... 57 | - Loading predictions from data/names/gpt/it.gpt-3.5.json... 58 | # of entities in the references: 995 59 | # of entities in the predictions: 937 60 | --------------------------- 61 | Coverage - P = 59.2 62 | Coverage - R = 49.1 63 | Coverage - F1 = 53.7 64 | =========================== 65 | 66 | =========================== 67 | Starting evaluation... 68 | --------------------------- 69 | - Loading references from data/names/gold/ja.json... 70 | - Loading predictions from data/names/gpt/ja.gpt-3.5.json... 71 | # of entities in the references: 997 72 | # of entities in the predictions: 814 73 | --------------------------- 74 | Coverage - P = 33.4 75 | Coverage - R = 25.5 76 | Coverage - F1 = 28.9 77 | =========================== 78 | 79 | =========================== 80 | Starting evaluation... 81 | --------------------------- 82 | - Loading references from data/names/gold/ko.json... 83 | - Loading predictions from data/names/gpt/ko.gpt-3.5.json... 84 | # of entities in the references: 993 85 | # of entities in the predictions: 851 86 | --------------------------- 87 | Coverage - P = 39.3 88 | Coverage - R = 26.9 89 | Coverage - F1 = 31.9 90 | =========================== 91 | 92 | =========================== 93 | Starting evaluation... 94 | --------------------------- 95 | - Loading references from data/names/gold/ru.json... 96 | - Loading predictions from data/names/gpt/ru.gpt-3.5.json... 97 | # of entities in the references: 999 98 | # of entities in the predictions: 903 99 | --------------------------- 100 | Coverage - P = 43.1 101 | Coverage - R = 32.2 102 | Coverage - F1 = 36.8 103 | =========================== 104 | 105 | =========================== 106 | Starting evaluation... 107 | --------------------------- 108 | - Loading references from data/names/gold/zh.json... 109 | - Loading predictions from data/names/gpt/zh.gpt-3.5.json... 110 | # of entities in the references: 974 111 | # of entities in the predictions: 883 112 | --------------------------- 113 | Coverage - P = 35.9 114 | Coverage - R = 24.7 115 | Coverage - F1 = 29.2 116 | =========================== 117 | 118 | -------------------------------------------------------------------------------- /results/coverage/m-nta.gpt-3.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/m-nta/with_gpt-3/ar.m-nta.json... 6 | # of entities in the references: 999 7 | # of entities in the predictions: 745 8 | --------------------------- 9 | Coverage - P = 68.4 10 | Coverage - R = 29.6 11 | Coverage - F1 = 41.3 12 | =========================== 13 | 14 | =========================== 15 | Starting evaluation... 16 | --------------------------- 17 | - Loading references from data/names/gold/de.json... 18 | - Loading predictions from data/names/m-nta/with_gpt-3/de.m-nta.json... 19 | # of entities in the references: 990 20 | # of entities in the predictions: 866 21 | --------------------------- 22 | Coverage - P = 72.8 23 | Coverage - R = 47.5 24 | Coverage - F1 = 57.5 25 | =========================== 26 | 27 | =========================== 28 | Starting evaluation... 29 | --------------------------- 30 | - Loading references from data/names/gold/en.json... 31 | - Loading predictions from data/names/m-nta/with_gpt-3/en.m-nta.json... 32 | # of entities in the references: 1,000 33 | # of entities in the predictions: 527 34 | --------------------------- 35 | Coverage - P = 89.7 36 | Coverage - R = 26.8 37 | Coverage - F1 = 41.3 38 | =========================== 39 | 40 | =========================== 41 | Starting evaluation... 42 | --------------------------- 43 | - Loading references from data/names/gold/es.json... 44 | - Loading predictions from data/names/m-nta/with_gpt-3/es.m-nta.json... 45 | # of entities in the references: 999 46 | # of entities in the predictions: 886 47 | --------------------------- 48 | Coverage - P = 79.4 49 | Coverage - R = 42.5 50 | Coverage - F1 = 55.4 51 | =========================== 52 | 53 | =========================== 54 | Starting evaluation... 55 | --------------------------- 56 | - Loading references from data/names/gold/fr.json... 57 | - Loading predictions from data/names/m-nta/with_gpt-3/fr.m-nta.json... 58 | # of entities in the references: 997 59 | # of entities in the predictions: 878 60 | --------------------------- 61 | Coverage - P = 71.7 62 | Coverage - R = 47.5 63 | Coverage - F1 = 57.1 64 | =========================== 65 | 66 | =========================== 67 | Starting evaluation... 68 | --------------------------- 69 | - Loading references from data/names/gold/it.json... 70 | - Loading predictions from data/names/m-nta/with_gpt-3/it.m-nta.json... 71 | # of entities in the references: 995 72 | # of entities in the predictions: 885 73 | --------------------------- 74 | Coverage - P = 72.0 75 | Coverage - R = 53.3 76 | Coverage - F1 = 61.3 77 | =========================== 78 | 79 | =========================== 80 | Starting evaluation... 81 | --------------------------- 82 | - Loading references from data/names/gold/ja.json... 83 | - Loading predictions from data/names/m-nta/with_gpt-3/ja.m-nta.json... 84 | # of entities in the references: 997 85 | # of entities in the predictions: 682 86 | --------------------------- 87 | Coverage - P = 49.7 88 | Coverage - R = 25.9 89 | Coverage - F1 = 34.0 90 | =========================== 91 | 92 | =========================== 93 | Starting evaluation... 94 | --------------------------- 95 | - Loading references from data/names/gold/ko.json... 96 | - Loading predictions from data/names/m-nta/with_gpt-3/ko.m-nta.json... 97 | # of entities in the references: 993 98 | # of entities in the predictions: 797 99 | --------------------------- 100 | Coverage - P = 70.0 101 | Coverage - R = 38.9 102 | Coverage - F1 = 50.0 103 | =========================== 104 | 105 | =========================== 106 | Starting evaluation... 107 | --------------------------- 108 | - Loading references from data/names/gold/ru.json... 109 | - Loading predictions from data/names/m-nta/with_gpt-3/ru.m-nta.json... 110 | # of entities in the references: 999 111 | # of entities in the predictions: 803 112 | --------------------------- 113 | Coverage - P = 62.1 114 | Coverage - R = 34.2 115 | Coverage - F1 = 44.1 116 | =========================== 117 | 118 | =========================== 119 | Starting evaluation... 120 | --------------------------- 121 | - Loading references from data/names/gold/zh.json... 122 | - Loading predictions from data/names/m-nta/with_gpt-3/zh.m-nta.json... 123 | # of entities in the references: 974 124 | # of entities in the predictions: 739 125 | --------------------------- 126 | Coverage - P = 51.4 127 | Coverage - R = 26.2 128 | Coverage - F1 = 34.7 129 | =========================== 130 | 131 | -------------------------------------------------------------------------------- /results/coverage/m-nta.gpt-4.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/m-nta/with_gpt-4/ar.m-nta.json... 6 | # of entities in the references: 999 7 | # of entities in the predictions: 772 8 | --------------------------- 9 | Coverage - P = 68.2 10 | Coverage - R = 31.6 11 | Coverage - F1 = 43.2 12 | =========================== 13 | 14 | =========================== 15 | Starting evaluation... 16 | --------------------------- 17 | - Loading references from data/names/gold/de.json... 18 | - Loading predictions from data/names/m-nta/with_gpt-4/de.m-nta.json... 19 | # of entities in the references: 990 20 | # of entities in the predictions: 861 21 | --------------------------- 22 | Coverage - P = 71.4 23 | Coverage - R = 47.6 24 | Coverage - F1 = 57.1 25 | =========================== 26 | 27 | =========================== 28 | Starting evaluation... 29 | --------------------------- 30 | - Loading references from data/names/gold/en.json... 31 | - Loading predictions from data/names/m-nta/with_gpt-4/en.m-nta.json... 32 | # of entities in the references: 1,000 33 | # of entities in the predictions: 527 34 | --------------------------- 35 | Coverage - P = 89.7 36 | Coverage - R = 26.8 37 | Coverage - F1 = 41.3 38 | =========================== 39 | 40 | =========================== 41 | Starting evaluation... 42 | --------------------------- 43 | - Loading references from data/names/gold/es.json... 44 | - Loading predictions from data/names/m-nta/with_gpt-4/es.m-nta.json... 45 | # of entities in the references: 999 46 | # of entities in the predictions: 886 47 | --------------------------- 48 | Coverage - P = 77.5 49 | Coverage - R = 43.6 50 | Coverage - F1 = 55.8 51 | =========================== 52 | 53 | =========================== 54 | Starting evaluation... 55 | --------------------------- 56 | - Loading references from data/names/gold/fr.json... 57 | - Loading predictions from data/names/m-nta/with_gpt-4/fr.m-nta.json... 58 | # of entities in the references: 997 59 | # of entities in the predictions: 877 60 | --------------------------- 61 | Coverage - P = 70.9 62 | Coverage - R = 48.1 63 | Coverage - F1 = 57.4 64 | =========================== 65 | 66 | =========================== 67 | Starting evaluation... 68 | --------------------------- 69 | - Loading references from data/names/gold/it.json... 70 | - Loading predictions from data/names/m-nta/with_gpt-4/it.m-nta.json... 71 | # of entities in the references: 995 72 | # of entities in the predictions: 885 73 | --------------------------- 74 | Coverage - P = 72.7 75 | Coverage - R = 53.5 76 | Coverage - F1 = 61.7 77 | =========================== 78 | 79 | =========================== 80 | Starting evaluation... 81 | --------------------------- 82 | - Loading references from data/names/gold/ja.json... 83 | - Loading predictions from data/names/m-nta/with_gpt-4/ja.m-nta.json... 84 | # of entities in the references: 997 85 | # of entities in the predictions: 683 86 | --------------------------- 87 | Coverage - P = 50.0 88 | Coverage - R = 27.1 89 | Coverage - F1 = 35.2 90 | =========================== 91 | 92 | =========================== 93 | Starting evaluation... 94 | --------------------------- 95 | - Loading references from data/names/gold/ko.json... 96 | - Loading predictions from data/names/m-nta/with_gpt-4/ko.m-nta.json... 97 | # of entities in the references: 993 98 | # of entities in the predictions: 809 99 | --------------------------- 100 | Coverage - P = 68.8 101 | Coverage - R = 40.8 102 | Coverage - F1 = 51.2 103 | =========================== 104 | 105 | =========================== 106 | Starting evaluation... 107 | --------------------------- 108 | - Loading references from data/names/gold/ru.json... 109 | - Loading predictions from data/names/m-nta/with_gpt-4/ru.m-nta.json... 110 | # of entities in the references: 999 111 | # of entities in the predictions: 816 112 | --------------------------- 113 | Coverage - P = 62.4 114 | Coverage - R = 35.6 115 | Coverage - F1 = 45.3 116 | =========================== 117 | 118 | =========================== 119 | Starting evaluation... 120 | --------------------------- 121 | - Loading references from data/names/gold/zh.json... 122 | - Loading predictions from data/names/m-nta/with_gpt-4/zh.m-nta.json... 123 | # of entities in the references: 974 124 | # of entities in the predictions: 748 125 | --------------------------- 126 | Coverage - P = 52.4 127 | Coverage - R = 28.4 128 | Coverage - F1 = 36.8 129 | =========================== 130 | 131 | -------------------------------------------------------------------------------- /results/coverage/m-nta.gpt-3.5.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/m-nta/with_gpt-3.5/ar.m-nta.json... 6 | # of entities in the references: 999 7 | # of entities in the predictions: 769 8 | --------------------------- 9 | Coverage - P = 68.3 10 | Coverage - R = 31.1 11 | Coverage - F1 = 42.7 12 | =========================== 13 | 14 | =========================== 15 | Starting evaluation... 16 | --------------------------- 17 | - Loading references from data/names/gold/de.json... 18 | - Loading predictions from data/names/m-nta/with_gpt-3.5/de.m-nta.json... 19 | # of entities in the references: 990 20 | # of entities in the predictions: 858 21 | --------------------------- 22 | Coverage - P = 72.7 23 | Coverage - R = 47.6 24 | Coverage - F1 = 57.5 25 | =========================== 26 | 27 | =========================== 28 | Starting evaluation... 29 | --------------------------- 30 | - Loading references from data/names/gold/en.json... 31 | - Loading predictions from data/names/m-nta/with_gpt-3.5/en.m-nta.json... 32 | # of entities in the references: 1,000 33 | # of entities in the predictions: 527 34 | --------------------------- 35 | Coverage - P = 89.7 36 | Coverage - R = 26.8 37 | Coverage - F1 = 41.3 38 | =========================== 39 | 40 | =========================== 41 | Starting evaluation... 42 | --------------------------- 43 | - Loading references from data/names/gold/es.json... 44 | - Loading predictions from data/names/m-nta/with_gpt-3.5/es.m-nta.json... 45 | # of entities in the references: 999 46 | # of entities in the predictions: 889 47 | --------------------------- 48 | Coverage - P = 77.2 49 | Coverage - R = 43.4 50 | Coverage - F1 = 55.6 51 | =========================== 52 | 53 | =========================== 54 | Starting evaluation... 55 | --------------------------- 56 | - Loading references from data/names/gold/fr.json... 57 | - Loading predictions from data/names/m-nta/with_gpt-3.5/fr.m-nta.json... 58 | # of entities in the references: 997 59 | # of entities in the predictions: 878 60 | --------------------------- 61 | Coverage - P = 70.7 62 | Coverage - R = 48.2 63 | Coverage - F1 = 57.3 64 | =========================== 65 | 66 | =========================== 67 | Starting evaluation... 68 | --------------------------- 69 | - Loading references from data/names/gold/it.json... 70 | - Loading predictions from data/names/m-nta/with_gpt-3.5/it.m-nta.json... 71 | # of entities in the references: 995 72 | # of entities in the predictions: 883 73 | --------------------------- 74 | Coverage - P = 72.9 75 | Coverage - R = 53.5 76 | Coverage - F1 = 61.7 77 | =========================== 78 | 79 | =========================== 80 | Starting evaluation... 81 | --------------------------- 82 | - Loading references from data/names/gold/ja.json... 83 | - Loading predictions from data/names/m-nta/with_gpt-3.5/ja.m-nta.json... 84 | # of entities in the references: 997 85 | # of entities in the predictions: 686 86 | --------------------------- 87 | Coverage - P = 50.5 88 | Coverage - R = 27.0 89 | Coverage - F1 = 35.2 90 | =========================== 91 | 92 | =========================== 93 | Starting evaluation... 94 | --------------------------- 95 | - Loading references from data/names/gold/ko.json... 96 | - Loading predictions from data/names/m-nta/with_gpt-3.5/ko.m-nta.json... 97 | # of entities in the references: 993 98 | # of entities in the predictions: 808 99 | --------------------------- 100 | Coverage - P = 68.7 101 | Coverage - R = 40.0 102 | Coverage - F1 = 50.6 103 | =========================== 104 | 105 | =========================== 106 | Starting evaluation... 107 | --------------------------- 108 | - Loading references from data/names/gold/ru.json... 109 | - Loading predictions from data/names/m-nta/with_gpt-3.5/ru.m-nta.json... 110 | # of entities in the references: 999 111 | # of entities in the predictions: 811 112 | --------------------------- 113 | Coverage - P = 62.3 114 | Coverage - R = 34.9 115 | Coverage - F1 = 44.8 116 | =========================== 117 | 118 | =========================== 119 | Starting evaluation... 120 | --------------------------- 121 | - Loading references from data/names/gold/zh.json... 122 | - Loading predictions from data/names/m-nta/with_gpt-3.5/zh.m-nta.json... 123 | # of entities in the references: 974 124 | # of entities in the predictions: 749 125 | --------------------------- 126 | Coverage - P = 51.8 127 | Coverage - R = 27.7 128 | Coverage - F1 = 36.1 129 | =========================== 130 | 131 | -------------------------------------------------------------------------------- /results/precision/gpt-3.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/gpt/ar.gpt-3.json... 6 | # of entities: 1,000 7 | # of entities with correct names: 1,000 8 | # of entities with incorrect names: 1,000 9 | # of entities in the predictions: 853 10 | --------------------------- 11 | Total incorrect names: 320 12 | Identified incorrect names: 305 13 | Total correct names: 3,893 14 | Correct names classified as incorrect: 3,322 15 | --------------------------- 16 | Precision - P = 20.8 17 | Precision - R = 95.3 18 | Precision - F1 = 34.1 19 | =========================== 20 | 21 | =========================== 22 | Starting evaluation... 23 | --------------------------- 24 | - Loading references from data/names/gold/de.json... 25 | - Loading predictions from data/names/gpt/de.gpt-3.json... 26 | # of entities: 1,000 27 | # of entities with correct names: 999 28 | # of entities with incorrect names: 999 29 | # of entities in the predictions: 932 30 | --------------------------- 31 | Total incorrect names: 491 32 | Identified incorrect names: 449 33 | Total correct names: 3,007 34 | Correct names classified as incorrect: 1,695 35 | --------------------------- 36 | Precision - P = 50.3 37 | Precision - R = 91.4 38 | Precision - F1 = 64.9 39 | =========================== 40 | 41 | =========================== 42 | Starting evaluation... 43 | --------------------------- 44 | - Loading references from data/names/gold/es.json... 45 | - Loading predictions from data/names/gpt/es.gpt-3.json... 46 | # of entities: 1,000 47 | # of entities with correct names: 1,000 48 | # of entities with incorrect names: 1,000 49 | # of entities in the predictions: 938 50 | --------------------------- 51 | Total incorrect names: 571 52 | Identified incorrect names: 512 53 | Total correct names: 3,749 54 | Correct names classified as incorrect: 2,302 55 | --------------------------- 56 | Precision - P = 45.3 57 | Precision - R = 89.7 58 | Precision - F1 = 60.2 59 | =========================== 60 | 61 | =========================== 62 | Starting evaluation... 63 | --------------------------- 64 | - Loading references from data/names/gold/fr.json... 65 | - Loading predictions from data/names/gpt/fr.gpt-3.json... 66 | # of entities: 1,000 67 | # of entities with correct names: 1,000 68 | # of entities with incorrect names: 1,000 69 | # of entities in the predictions: 936 70 | --------------------------- 71 | Total incorrect names: 533 72 | Identified incorrect names: 455 73 | Total correct names: 3,015 74 | Correct names classified as incorrect: 1,736 75 | --------------------------- 76 | Precision - P = 48.9 77 | Precision - R = 85.4 78 | Precision - F1 = 62.2 79 | =========================== 80 | 81 | =========================== 82 | Starting evaluation... 83 | --------------------------- 84 | - Loading references from data/names/gold/it.json... 85 | - Loading predictions from data/names/gpt/it.gpt-3.json... 86 | # of entities: 999 87 | # of entities with correct names: 999 88 | # of entities with incorrect names: 999 89 | # of entities in the predictions: 937 90 | --------------------------- 91 | Total incorrect names: 267 92 | Identified incorrect names: 221 93 | Total correct names: 2,889 94 | Correct names classified as incorrect: 1,539 95 | --------------------------- 96 | Precision - P = 49.8 97 | Precision - R = 82.8 98 | Precision - F1 = 62.2 99 | =========================== 100 | 101 | =========================== 102 | Starting evaluation... 103 | --------------------------- 104 | - Loading references from data/names/gold/ja.json... 105 | - Loading predictions from data/names/gpt/ja.gpt-3.json... 106 | # of entities: 1,000 107 | # of entities with correct names: 1,000 108 | # of entities with incorrect names: 1,000 109 | # of entities in the predictions: 814 110 | --------------------------- 111 | Total incorrect names: 507 112 | Identified incorrect names: 489 113 | Total correct names: 2,492 114 | Correct names classified as incorrect: 1,993 115 | --------------------------- 116 | Precision - P = 32.9 117 | Precision - R = 96.4 118 | Precision - F1 = 49.1 119 | =========================== 120 | 121 | =========================== 122 | Starting evaluation... 123 | --------------------------- 124 | - Loading references from data/names/gold/ko.json... 125 | - Loading predictions from data/names/gpt/ko.gpt-3.json... 126 | # of entities: 1,000 127 | # of entities with correct names: 1,000 128 | # of entities with incorrect names: 1,000 129 | # of entities in the predictions: 851 130 | --------------------------- 131 | Total incorrect names: 330 132 | Identified incorrect names: 298 133 | Total correct names: 3,544 134 | Correct names classified as incorrect: 2,758 135 | --------------------------- 136 | Precision - P = 28.0 137 | Precision - R = 90.3 138 | Precision - F1 = 42.7 139 | =========================== 140 | 141 | =========================== 142 | Starting evaluation... 143 | --------------------------- 144 | - Loading references from data/names/gold/ru.json... 145 | - Loading predictions from data/names/gpt/ru.gpt-3.json... 146 | # of entities: 1,000 147 | # of entities with correct names: 1,000 148 | # of entities with incorrect names: 1,000 149 | # of entities in the predictions: 903 150 | --------------------------- 151 | Total incorrect names: 614 152 | Identified incorrect names: 567 153 | Total correct names: 3,287 154 | Correct names classified as incorrect: 2,383 155 | --------------------------- 156 | Precision - P = 37.7 157 | Precision - R = 92.3 158 | Precision - F1 = 53.5 159 | =========================== 160 | 161 | =========================== 162 | Starting evaluation... 163 | --------------------------- 164 | - Loading references from data/names/gold/zh.json... 165 | - Loading predictions from data/names/gpt/zh.gpt-3.json... 166 | # of entities: 1,000 167 | # of entities with correct names: 1,000 168 | # of entities with incorrect names: 1,000 169 | # of entities in the predictions: 883 170 | --------------------------- 171 | Total incorrect names: 811 172 | Identified incorrect names: 779 173 | Total correct names: 3,277 174 | Correct names classified as incorrect: 2,645 175 | --------------------------- 176 | Precision - P = 34.5 177 | Precision - R = 96.1 178 | Precision - F1 = 50.8 179 | =========================== 180 | 181 | -------------------------------------------------------------------------------- /results/precision/gpt-4.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/gpt/ar.gpt-4.json... 6 | # of entities: 1,000 7 | # of entities with correct names: 1,000 8 | # of entities with incorrect names: 1,000 9 | # of entities in the predictions: 853 10 | --------------------------- 11 | Total incorrect names: 320 12 | Identified incorrect names: 298 13 | Total correct names: 3,893 14 | Correct names classified as incorrect: 2,977 15 | --------------------------- 16 | Precision - P = 28.8 17 | Precision - R = 93.1 18 | Precision - F1 = 44.0 19 | =========================== 20 | 21 | =========================== 22 | Starting evaluation... 23 | --------------------------- 24 | - Loading references from data/names/gold/de.json... 25 | - Loading predictions from data/names/gpt/de.gpt-4.json... 26 | # of entities: 1,000 27 | # of entities with correct names: 999 28 | # of entities with incorrect names: 999 29 | # of entities in the predictions: 932 30 | --------------------------- 31 | Total incorrect names: 491 32 | Identified incorrect names: 452 33 | Total correct names: 3,007 34 | Correct names classified as incorrect: 1,654 35 | --------------------------- 36 | Precision - P = 51.6 37 | Precision - R = 92.1 38 | Precision - F1 = 66.1 39 | =========================== 40 | 41 | =========================== 42 | Starting evaluation... 43 | --------------------------- 44 | - Loading references from data/names/gold/es.json... 45 | - Loading predictions from data/names/gpt/es.gpt-4.json... 46 | # of entities: 1,000 47 | # of entities with correct names: 1,000 48 | # of entities with incorrect names: 1,000 49 | # of entities in the predictions: 938 50 | --------------------------- 51 | Total incorrect names: 571 52 | Identified incorrect names: 511 53 | Total correct names: 3,749 54 | Correct names classified as incorrect: 2,162 55 | --------------------------- 56 | Precision - P = 48.6 57 | Precision - R = 89.5 58 | Precision - F1 = 63.0 59 | =========================== 60 | 61 | =========================== 62 | Starting evaluation... 63 | --------------------------- 64 | - Loading references from data/names/gold/fr.json... 65 | - Loading predictions from data/names/gpt/fr.gpt-4.json... 66 | # of entities: 1,000 67 | # of entities with correct names: 1,000 68 | # of entities with incorrect names: 1,000 69 | # of entities in the predictions: 936 70 | --------------------------- 71 | Total incorrect names: 533 72 | Identified incorrect names: 444 73 | Total correct names: 3,015 74 | Correct names classified as incorrect: 1,633 75 | --------------------------- 76 | Precision - P = 51.5 77 | Precision - R = 83.3 78 | Precision - F1 = 63.6 79 | =========================== 80 | 81 | =========================== 82 | Starting evaluation... 83 | --------------------------- 84 | - Loading references from data/names/gold/it.json... 85 | - Loading predictions from data/names/gpt/it.gpt-4.json... 86 | # of entities: 999 87 | # of entities with correct names: 999 88 | # of entities with incorrect names: 999 89 | # of entities in the predictions: 937 90 | --------------------------- 91 | Total incorrect names: 267 92 | Identified incorrect names: 230 93 | Total correct names: 2,889 94 | Correct names classified as incorrect: 1,446 95 | --------------------------- 96 | Precision - P = 53.0 97 | Precision - R = 86.1 98 | Precision - F1 = 65.6 99 | =========================== 100 | 101 | =========================== 102 | Starting evaluation... 103 | --------------------------- 104 | - Loading references from data/names/gold/ja.json... 105 | - Loading predictions from data/names/gpt/ja.gpt-4.json... 106 | # of entities: 1,000 107 | # of entities with correct names: 1,000 108 | # of entities with incorrect names: 1,000 109 | # of entities in the predictions: 814 110 | --------------------------- 111 | Total incorrect names: 507 112 | Identified incorrect names: 486 113 | Total correct names: 2,492 114 | Correct names classified as incorrect: 1,783 115 | --------------------------- 116 | Precision - P = 39.8 117 | Precision - R = 95.9 118 | Precision - F1 = 56.3 119 | =========================== 120 | 121 | =========================== 122 | Starting evaluation... 123 | --------------------------- 124 | - Loading references from data/names/gold/ko.json... 125 | - Loading predictions from data/names/gpt/ko.gpt-4.json... 126 | # of entities: 1,000 127 | # of entities with correct names: 1,000 128 | # of entities with incorrect names: 1,000 129 | # of entities in the predictions: 851 130 | --------------------------- 131 | Total incorrect names: 330 132 | Identified incorrect names: 293 133 | Total correct names: 3,544 134 | Correct names classified as incorrect: 2,529 135 | --------------------------- 136 | Precision - P = 33.8 137 | Precision - R = 88.8 138 | Precision - F1 = 48.9 139 | =========================== 140 | 141 | =========================== 142 | Starting evaluation... 143 | --------------------------- 144 | - Loading references from data/names/gold/ru.json... 145 | - Loading predictions from data/names/gpt/ru.gpt-4.json... 146 | # of entities: 1,000 147 | # of entities with correct names: 1,000 148 | # of entities with incorrect names: 1,000 149 | # of entities in the predictions: 903 150 | --------------------------- 151 | Total incorrect names: 614 152 | Identified incorrect names: 565 153 | Total correct names: 3,287 154 | Correct names classified as incorrect: 2,180 155 | --------------------------- 156 | Precision - P = 42.9 157 | Precision - R = 92.0 158 | Precision - F1 = 58.5 159 | =========================== 160 | 161 | =========================== 162 | Starting evaluation... 163 | --------------------------- 164 | - Loading references from data/names/gold/zh.json... 165 | - Loading predictions from data/names/gpt/zh.gpt-4.json... 166 | # of entities: 1,000 167 | # of entities with correct names: 1,000 168 | # of entities with incorrect names: 1,000 169 | # of entities in the predictions: 883 170 | --------------------------- 171 | Total incorrect names: 811 172 | Identified incorrect names: 787 173 | Total correct names: 3,277 174 | Correct names classified as incorrect: 2,433 175 | --------------------------- 176 | Precision - P = 39.9 177 | Precision - R = 97.0 178 | Precision - F1 = 56.5 179 | =========================== 180 | 181 | -------------------------------------------------------------------------------- /results/precision/gpt-3.5.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/gpt/ar.gpt-3.5.json... 6 | # of entities: 1,000 7 | # of entities with correct names: 1,000 8 | # of entities with incorrect names: 1,000 9 | # of entities in the predictions: 853 10 | --------------------------- 11 | Total incorrect names: 320 12 | Identified incorrect names: 299 13 | Total correct names: 3,893 14 | Correct names classified as incorrect: 3,049 15 | --------------------------- 16 | Precision - P = 27.1 17 | Precision - R = 93.4 18 | Precision - F1 = 42.1 19 | =========================== 20 | 21 | =========================== 22 | Starting evaluation... 23 | --------------------------- 24 | - Loading references from data/names/gold/de.json... 25 | - Loading predictions from data/names/gpt/de.gpt-3.5.json... 26 | # of entities: 1,000 27 | # of entities with correct names: 999 28 | # of entities with incorrect names: 999 29 | # of entities in the predictions: 932 30 | --------------------------- 31 | Total incorrect names: 491 32 | Identified incorrect names: 451 33 | Total correct names: 3,007 34 | Correct names classified as incorrect: 1,647 35 | --------------------------- 36 | Precision - P = 51.8 37 | Precision - R = 91.9 38 | Precision - F1 = 66.2 39 | =========================== 40 | 41 | =========================== 42 | Starting evaluation... 43 | --------------------------- 44 | - Loading references from data/names/gold/es.json... 45 | - Loading predictions from data/names/gpt/es.gpt-3.5.json... 46 | # of entities: 1,000 47 | # of entities with correct names: 1,000 48 | # of entities with incorrect names: 1,000 49 | # of entities in the predictions: 938 50 | --------------------------- 51 | Total incorrect names: 571 52 | Identified incorrect names: 512 53 | Total correct names: 3,749 54 | Correct names classified as incorrect: 2,155 55 | --------------------------- 56 | Precision - P = 48.8 57 | Precision - R = 89.7 58 | Precision - F1 = 63.2 59 | =========================== 60 | 61 | =========================== 62 | Starting evaluation... 63 | --------------------------- 64 | - Loading references from data/names/gold/fr.json... 65 | - Loading predictions from data/names/gpt/fr.gpt-3.5.json... 66 | # of entities: 1,000 67 | # of entities with correct names: 1,000 68 | # of entities with incorrect names: 1,000 69 | # of entities in the predictions: 936 70 | --------------------------- 71 | Total incorrect names: 533 72 | Identified incorrect names: 439 73 | Total correct names: 3,015 74 | Correct names classified as incorrect: 1,631 75 | --------------------------- 76 | Precision - P = 51.4 77 | Precision - R = 82.4 78 | Precision - F1 = 63.3 79 | =========================== 80 | 81 | =========================== 82 | Starting evaluation... 83 | --------------------------- 84 | - Loading references from data/names/gold/it.json... 85 | - Loading predictions from data/names/gpt/it.gpt-3.5.json... 86 | # of entities: 999 87 | # of entities with correct names: 999 88 | # of entities with incorrect names: 999 89 | # of entities in the predictions: 937 90 | --------------------------- 91 | Total incorrect names: 267 92 | Identified incorrect names: 227 93 | Total correct names: 2,889 94 | Correct names classified as incorrect: 1,460 95 | --------------------------- 96 | Precision - P = 52.5 97 | Precision - R = 85.0 98 | Precision - F1 = 64.9 99 | =========================== 100 | 101 | =========================== 102 | Starting evaluation... 103 | --------------------------- 104 | - Loading references from data/names/gold/ja.json... 105 | - Loading predictions from data/names/gpt/ja.gpt-3.5.json... 106 | # of entities: 1,000 107 | # of entities with correct names: 1,000 108 | # of entities with incorrect names: 1,000 109 | # of entities in the predictions: 814 110 | --------------------------- 111 | Total incorrect names: 507 112 | Identified incorrect names: 488 113 | Total correct names: 2,492 114 | Correct names classified as incorrect: 1,843 115 | --------------------------- 116 | Precision - P = 37.9 117 | Precision - R = 96.3 118 | Precision - F1 = 54.4 119 | =========================== 120 | 121 | =========================== 122 | Starting evaluation... 123 | --------------------------- 124 | - Loading references from data/names/gold/ko.json... 125 | - Loading predictions from data/names/gpt/ko.gpt-3.5.json... 126 | # of entities: 1,000 127 | # of entities with correct names: 1,000 128 | # of entities with incorrect names: 1,000 129 | # of entities in the predictions: 851 130 | --------------------------- 131 | Total incorrect names: 330 132 | Identified incorrect names: 294 133 | Total correct names: 3,544 134 | Correct names classified as incorrect: 2,591 135 | --------------------------- 136 | Precision - P = 32.2 137 | Precision - R = 89.1 138 | Precision - F1 = 47.3 139 | =========================== 140 | 141 | =========================== 142 | Starting evaluation... 143 | --------------------------- 144 | - Loading references from data/names/gold/ru.json... 145 | - Loading predictions from data/names/gpt/ru.gpt-3.5.json... 146 | # of entities: 1,000 147 | # of entities with correct names: 1,000 148 | # of entities with incorrect names: 1,000 149 | # of entities in the predictions: 903 150 | --------------------------- 151 | Total incorrect names: 614 152 | Identified incorrect names: 554 153 | Total correct names: 3,287 154 | Correct names classified as incorrect: 2,246 155 | --------------------------- 156 | Precision - P = 40.9 157 | Precision - R = 90.2 158 | Precision - F1 = 56.3 159 | =========================== 160 | 161 | =========================== 162 | Starting evaluation... 163 | --------------------------- 164 | - Loading references from data/names/gold/zh.json... 165 | - Loading predictions from data/names/gpt/zh.gpt-3.5.json... 166 | # of entities: 1,000 167 | # of entities with correct names: 1,000 168 | # of entities with incorrect names: 1,000 169 | # of entities in the predictions: 883 170 | --------------------------- 171 | Total incorrect names: 811 172 | Identified incorrect names: 784 173 | Total correct names: 3,277 174 | Correct names classified as incorrect: 2,461 175 | --------------------------- 176 | Precision - P = 39.1 177 | Precision - R = 96.7 178 | Precision - F1 = 55.7 179 | =========================== 180 | 181 | -------------------------------------------------------------------------------- /src/evaluation/evaluate_precision.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | """Evaluate a set of translations against a set of references. 4 | 5 | Reads the references contained in --references_path and the 6 | translations contained in --predictions_path and performs an 7 | evaluation of the translation using a scorer specified by --scorer. 8 | 9 | Typical usage example: 10 | 11 | python src/evaluation/evaluate_translations.py \ 12 | --references_path references.json \ 13 | --predictions_path predictions.json 14 | 15 | """ 16 | 17 | import argparse 18 | import json 19 | from typing import Dict, List 20 | 21 | 22 | def normalize_value(value: str) -> str: 23 | value = value.lower() 24 | value = "".join([i for i in value if i.isalnum() or i == " "]) 25 | return value 26 | 27 | 28 | def read_file(input_path: str, field_name: str) -> Dict[str, List[str]]: 29 | data: Dict[int, List[str]] = {} 30 | 31 | with open(input_path) as f: 32 | for line in f: 33 | line = line.strip() 34 | if not line: 35 | continue 36 | 37 | line_data = json.loads(line) 38 | if field_name in line_data: 39 | data[line_data["wikidata_id"]] = [ 40 | normalize_value(v) for v in line_data[field_name] 41 | ] 42 | 43 | return data 44 | 45 | 46 | def read_predictions(input_path: str, threshold: int) -> Dict[str, List[str]]: 47 | data: Dict[int, List[str]] = {} 48 | 49 | with open(input_path) as f: 50 | for line in f: 51 | line = line.strip() 52 | if not line: 53 | continue 54 | 55 | line_data = json.loads(line) 56 | wikidata_id = line_data["wikidata_id"] 57 | values = line_data["values"] 58 | values = [normalize_value(v) for s, v, *_ in values if s >= threshold] 59 | values = set(values) 60 | 61 | if values: 62 | data[wikidata_id] = values 63 | 64 | return data 65 | 66 | 67 | if __name__ == "__main__": 68 | parser = argparse.ArgumentParser() 69 | 70 | parser.add_argument( 71 | "--references_path", 72 | type=str, 73 | required=True, 74 | help="Path to the JSONL file containing the references.", 75 | ) 76 | parser.add_argument( 77 | "--predictions_path", 78 | type=str, 79 | required=True, 80 | help="Path to the JSONL file containing the predictions.", 81 | ) 82 | parser.add_argument( 83 | "--wikidata_ids_path", 84 | type=str, 85 | required=False, 86 | help="Allows the evaluation to be performed only on the subset of entities specified in this .txt file.", 87 | ) 88 | parser.add_argument( 89 | "--print_pairwise_scores", 90 | action="store_true", 91 | help="If set, prints reference-prediction pairs.", 92 | ) 93 | parser.add_argument( 94 | "--quiet", 95 | action="store_true", 96 | help="Use this flag to reduce the verbosity of the output.", 97 | ) 98 | parser.add_argument( 99 | "--score_threshold", 100 | type=int, 101 | default=0, 102 | help="Minimum score for the predictions to be evaluated.", 103 | ) 104 | 105 | args = parser.parse_args() 106 | 107 | print("===========================") 108 | print("Starting evaluation...") 109 | print("---------------------------") 110 | 111 | # Load references. 112 | print(f"- Loading references from {args.references_path}...") 113 | references_correct = read_file(args.references_path, field_name="correct_values") 114 | references_incorrect = read_file( 115 | args.references_path, field_name="incorrect_values" 116 | ) 117 | 118 | # Load predictions. 119 | print(f"- Loading predictions from {args.predictions_path}...") 120 | predictions = read_predictions(args.predictions_path, args.score_threshold) 121 | 122 | # Read Wikidata IDs if provided. 123 | if args.wikidata_ids_path: 124 | wikidata_ids = set() 125 | 126 | with open(args.wikidata_ids_path) as f: 127 | for line in f: 128 | line = line.strip() 129 | if not line: 130 | continue 131 | 132 | wikidata_id = line.strip() 133 | wikidata_ids.add(wikidata_id) 134 | 135 | num_entities: int = 0 136 | num_incorrect_names: int = 0 137 | num_correct_names: int = 0 138 | num_true_incorrect: int = 0 139 | num_false_incorrect: int = 0 140 | 141 | all_ids = set(references_correct.keys()) 142 | all_ids.update(set(references_incorrect.keys())) 143 | all_ids.update(set(predictions.keys())) 144 | 145 | for entity_id in sorted(all_ids): 146 | if args.wikidata_ids_path and entity_id not in wikidata_ids: 147 | continue 148 | 149 | num_entities += 1 150 | instance_predictions = predictions.get(entity_id, []) 151 | 152 | if entity_id in references_incorrect: 153 | incorrect_names = references_incorrect[entity_id] 154 | num_incorrect_names += len(incorrect_names) 155 | 156 | for incorrect_name in incorrect_names: 157 | if incorrect_name not in instance_predictions: 158 | num_true_incorrect += 1 159 | 160 | if entity_id in references_correct: 161 | correct_names = references_correct[entity_id] 162 | num_correct_names += len(correct_names) 163 | 164 | for correct_name in correct_names: 165 | if correct_name not in instance_predictions: 166 | num_false_incorrect += 1 167 | 168 | recall = num_true_incorrect / num_incorrect_names 169 | precision = (num_correct_names - num_false_incorrect + num_true_incorrect) / ( 170 | num_correct_names + num_incorrect_names 171 | ) 172 | f1 = 2 * precision * recall / (precision + recall) 173 | 174 | if not args.quiet: 175 | print(f" # of entities: {len(all_ids):,}") 176 | print(f" # of entities with correct names: {len(references_correct):,}") 177 | print(f" # of entities with incorrect names: {len(references_incorrect):,}") 178 | print(f" # of entities in the predictions: {len(predictions):,}") 179 | print("---------------------------") 180 | print(f" Total incorrect names: {num_incorrect_names:,}") 181 | print(f" Identified incorrect names: {num_true_incorrect:,}") 182 | print(f" Total correct names: {num_correct_names:,}") 183 | print(f" Correct names classified as incorrect: {num_false_incorrect:,}") 184 | print("---------------------------") 185 | print(f" Precision - P = {100 * precision:0.1f}") 186 | print(f" Precision - R = {100 * recall:0.1f}") 187 | print(f" Precision - F1 = {100 * f1:0.1f}") 188 | print("===========================") 189 | print() 190 | 191 | if args.quiet: 192 | print(f"{100.*recall:0.1f} {100.*precision:0.1f} {100 * f1:0.1f}") 193 | -------------------------------------------------------------------------------- /results/precision/m-nta.gpt-4.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/m-nta/with_gpt-4/ar.m-nta.json... 6 | # of entities: 1,000 7 | # of entities with correct names: 1,000 8 | # of entities with incorrect names: 1,000 9 | # of entities in the predictions: 869 10 | --------------------------- 11 | Total incorrect names: 320 12 | Identified incorrect names: 282 13 | Total correct names: 3,893 14 | Correct names classified as incorrect: 1,464 15 | --------------------------- 16 | Precision - P = 64.3 17 | Precision - R = 88.1 18 | Precision - F1 = 74.4 19 | =========================== 20 | 21 | =========================== 22 | Starting evaluation... 23 | --------------------------- 24 | - Loading references from data/names/gold/de.json... 25 | - Loading predictions from data/names/m-nta/with_gpt-4/de.m-nta.json... 26 | # of entities: 1,000 27 | # of entities with correct names: 999 28 | # of entities with incorrect names: 999 29 | # of entities in the predictions: 939 30 | --------------------------- 31 | Total incorrect names: 491 32 | Identified incorrect names: 409 33 | Total correct names: 3,007 34 | Correct names classified as incorrect: 881 35 | --------------------------- 36 | Precision - P = 72.5 37 | Precision - R = 83.3 38 | Precision - F1 = 77.5 39 | =========================== 40 | 41 | =========================== 42 | Starting evaluation... 43 | --------------------------- 44 | - Loading references from data/names/gold/en.json... 45 | - Loading predictions from data/names/m-nta/with_gpt-4/en.m-nta.json... 46 | # of entities: 1,000 47 | # of entities with correct names: 1,000 48 | # of entities with incorrect names: 1,000 49 | # of entities in the predictions: 776 50 | --------------------------- 51 | Total incorrect names: 219 52 | Identified incorrect names: 175 53 | Total correct names: 2,618 54 | Correct names classified as incorrect: 1,246 55 | --------------------------- 56 | Precision - P = 54.5 57 | Precision - R = 79.9 58 | Precision - F1 = 64.8 59 | =========================== 60 | 61 | =========================== 62 | Starting evaluation... 63 | --------------------------- 64 | - Loading references from data/names/gold/es.json... 65 | - Loading predictions from data/names/m-nta/with_gpt-4/es.m-nta.json... 66 | # of entities: 1,000 67 | # of entities with correct names: 1,000 68 | # of entities with incorrect names: 1,000 69 | # of entities in the predictions: 951 70 | --------------------------- 71 | Total incorrect names: 571 72 | Identified incorrect names: 447 73 | Total correct names: 3,749 74 | Correct names classified as incorrect: 1,083 75 | --------------------------- 76 | Precision - P = 72.1 77 | Precision - R = 78.3 78 | Precision - F1 = 75.0 79 | =========================== 80 | 81 | =========================== 82 | Starting evaluation... 83 | --------------------------- 84 | - Loading references from data/names/gold/fr.json... 85 | - Loading predictions from data/names/m-nta/with_gpt-4/fr.m-nta.json... 86 | # of entities: 1,000 87 | # of entities with correct names: 1,000 88 | # of entities with incorrect names: 1,000 89 | # of entities in the predictions: 946 90 | --------------------------- 91 | Total incorrect names: 533 92 | Identified incorrect names: 368 93 | Total correct names: 3,015 94 | Correct names classified as incorrect: 846 95 | --------------------------- 96 | Precision - P = 71.5 97 | Precision - R = 69.0 98 | Precision - F1 = 70.3 99 | =========================== 100 | 101 | =========================== 102 | Starting evaluation... 103 | --------------------------- 104 | - Loading references from data/names/gold/it.json... 105 | - Loading predictions from data/names/m-nta/with_gpt-4/it.m-nta.json... 106 | # of entities: 999 107 | # of entities with correct names: 999 108 | # of entities with incorrect names: 999 109 | # of entities in the predictions: 952 110 | --------------------------- 111 | Total incorrect names: 267 112 | Identified incorrect names: 193 113 | Total correct names: 2,889 114 | Correct names classified as incorrect: 585 115 | --------------------------- 116 | Precision - P = 79.1 117 | Precision - R = 72.3 118 | Precision - F1 = 75.5 119 | =========================== 120 | 121 | =========================== 122 | Starting evaluation... 123 | --------------------------- 124 | - Loading references from data/names/gold/ja.json... 125 | - Loading predictions from data/names/m-nta/with_gpt-4/ja.m-nta.json... 126 | # of entities: 1,000 127 | # of entities with correct names: 1,000 128 | # of entities with incorrect names: 1,000 129 | # of entities in the predictions: 829 130 | --------------------------- 131 | Total incorrect names: 507 132 | Identified incorrect names: 465 133 | Total correct names: 2,492 134 | Correct names classified as incorrect: 1,341 135 | --------------------------- 136 | Precision - P = 53.9 137 | Precision - R = 91.7 138 | Precision - F1 = 67.9 139 | =========================== 140 | 141 | =========================== 142 | Starting evaluation... 143 | --------------------------- 144 | - Loading references from data/names/gold/ko.json... 145 | - Loading predictions from data/names/m-nta/with_gpt-4/ko.m-nta.json... 146 | # of entities: 1,000 147 | # of entities with correct names: 1,000 148 | # of entities with incorrect names: 1,000 149 | # of entities in the predictions: 874 150 | --------------------------- 151 | Total incorrect names: 330 152 | Identified incorrect names: 268 153 | Total correct names: 3,544 154 | Correct names classified as incorrect: 989 155 | --------------------------- 156 | Precision - P = 72.9 157 | Precision - R = 81.2 158 | Precision - F1 = 76.8 159 | =========================== 160 | 161 | =========================== 162 | Starting evaluation... 163 | --------------------------- 164 | - Loading references from data/names/gold/ru.json... 165 | - Loading predictions from data/names/m-nta/with_gpt-4/ru.m-nta.json... 166 | # of entities: 1,000 167 | # of entities with correct names: 1,000 168 | # of entities with incorrect names: 1,000 169 | # of entities in the predictions: 923 170 | --------------------------- 171 | Total incorrect names: 614 172 | Identified incorrect names: 483 173 | Total correct names: 3,287 174 | Correct names classified as incorrect: 1,485 175 | --------------------------- 176 | Precision - P = 58.6 177 | Precision - R = 78.7 178 | Precision - F1 = 67.1 179 | =========================== 180 | 181 | =========================== 182 | Starting evaluation... 183 | --------------------------- 184 | - Loading references from data/names/gold/zh.json... 185 | - Loading predictions from data/names/m-nta/with_gpt-4/zh.m-nta.json... 186 | # of entities: 1,000 187 | # of entities with correct names: 1,000 188 | # of entities with incorrect names: 1,000 189 | # of entities in the predictions: 896 190 | --------------------------- 191 | Total incorrect names: 811 192 | Identified incorrect names: 752 193 | Total correct names: 3,277 194 | Correct names classified as incorrect: 1,622 195 | --------------------------- 196 | Precision - P = 58.9 197 | Precision - R = 92.7 198 | Precision - F1 = 72.0 199 | =========================== 200 | 201 | -------------------------------------------------------------------------------- /results/precision/m-nta.gpt-3.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/m-nta/with_gpt-3/ar.m-nta.json... 6 | # of entities: 1,000 7 | # of entities with correct names: 1,000 8 | # of entities with incorrect names: 1,000 9 | # of entities in the predictions: 869 10 | --------------------------- 11 | Total incorrect names: 320 12 | Identified incorrect names: 287 13 | Total correct names: 3,893 14 | Correct names classified as incorrect: 1,549 15 | --------------------------- 16 | Precision - P = 62.4 17 | Precision - R = 89.7 18 | Precision - F1 = 73.6 19 | =========================== 20 | 21 | =========================== 22 | Starting evaluation... 23 | --------------------------- 24 | - Loading references from data/names/gold/de.json... 25 | - Loading predictions from data/names/m-nta/with_gpt-3/de.m-nta.json... 26 | # of entities: 1,000 27 | # of entities with correct names: 999 28 | # of entities with incorrect names: 999 29 | # of entities in the predictions: 939 30 | --------------------------- 31 | Total incorrect names: 491 32 | Identified incorrect names: 406 33 | Total correct names: 3,007 34 | Correct names classified as incorrect: 875 35 | --------------------------- 36 | Precision - P = 72.6 37 | Precision - R = 82.7 38 | Precision - F1 = 77.3 39 | =========================== 40 | 41 | =========================== 42 | Starting evaluation... 43 | --------------------------- 44 | - Loading references from data/names/gold/en.json... 45 | - Loading predictions from data/names/m-nta/with_gpt-3/en.m-nta.json... 46 | # of entities: 1,000 47 | # of entities with correct names: 1,000 48 | # of entities with incorrect names: 1,000 49 | # of entities in the predictions: 776 50 | --------------------------- 51 | Total incorrect names: 219 52 | Identified incorrect names: 175 53 | Total correct names: 2,618 54 | Correct names classified as incorrect: 1,246 55 | --------------------------- 56 | Precision - P = 54.5 57 | Precision - R = 79.9 58 | Precision - F1 = 64.8 59 | =========================== 60 | 61 | =========================== 62 | Starting evaluation... 63 | --------------------------- 64 | - Loading references from data/names/gold/es.json... 65 | - Loading predictions from data/names/m-nta/with_gpt-3/es.m-nta.json... 66 | # of entities: 1,000 67 | # of entities with correct names: 1,000 68 | # of entities with incorrect names: 1,000 69 | # of entities in the predictions: 951 70 | --------------------------- 71 | Total incorrect names: 571 72 | Identified incorrect names: 445 73 | Total correct names: 3,749 74 | Correct names classified as incorrect: 1,098 75 | --------------------------- 76 | Precision - P = 71.7 77 | Precision - R = 77.9 78 | Precision - F1 = 74.7 79 | =========================== 80 | 81 | =========================== 82 | Starting evaluation... 83 | --------------------------- 84 | - Loading references from data/names/gold/fr.json... 85 | - Loading predictions from data/names/m-nta/with_gpt-3/fr.m-nta.json... 86 | # of entities: 1,000 87 | # of entities with correct names: 1,000 88 | # of entities with incorrect names: 1,000 89 | # of entities in the predictions: 946 90 | --------------------------- 91 | Total incorrect names: 533 92 | Identified incorrect names: 366 93 | Total correct names: 3,015 94 | Correct names classified as incorrect: 858 95 | --------------------------- 96 | Precision - P = 71.1 97 | Precision - R = 68.7 98 | Precision - F1 = 69.9 99 | =========================== 100 | 101 | =========================== 102 | Starting evaluation... 103 | --------------------------- 104 | - Loading references from data/names/gold/it.json... 105 | - Loading predictions from data/names/m-nta/with_gpt-3/it.m-nta.json... 106 | # of entities: 999 107 | # of entities with correct names: 999 108 | # of entities with incorrect names: 999 109 | # of entities in the predictions: 952 110 | --------------------------- 111 | Total incorrect names: 267 112 | Identified incorrect names: 191 113 | Total correct names: 2,889 114 | Correct names classified as incorrect: 585 115 | --------------------------- 116 | Precision - P = 79.1 117 | Precision - R = 71.5 118 | Precision - F1 = 75.1 119 | =========================== 120 | 121 | =========================== 122 | Starting evaluation... 123 | --------------------------- 124 | - Loading references from data/names/gold/ja.json... 125 | - Loading predictions from data/names/m-nta/with_gpt-3/ja.m-nta.json... 126 | # of entities: 1,000 127 | # of entities with correct names: 1,000 128 | # of entities with incorrect names: 1,000 129 | # of entities in the predictions: 829 130 | --------------------------- 131 | Total incorrect names: 507 132 | Identified incorrect names: 466 133 | Total correct names: 2,492 134 | Correct names classified as incorrect: 1,420 135 | --------------------------- 136 | Precision - P = 51.3 137 | Precision - R = 91.9 138 | Precision - F1 = 65.8 139 | =========================== 140 | 141 | =========================== 142 | Starting evaluation... 143 | --------------------------- 144 | - Loading references from data/names/gold/ko.json... 145 | - Loading predictions from data/names/m-nta/with_gpt-3/ko.m-nta.json... 146 | # of entities: 1,000 147 | # of entities with correct names: 1,000 148 | # of entities with incorrect names: 1,000 149 | # of entities in the predictions: 874 150 | --------------------------- 151 | Total incorrect names: 330 152 | Identified incorrect names: 269 153 | Total correct names: 3,544 154 | Correct names classified as incorrect: 1,017 155 | --------------------------- 156 | Precision - P = 72.2 157 | Precision - R = 81.5 158 | Precision - F1 = 76.6 159 | =========================== 160 | 161 | =========================== 162 | Starting evaluation... 163 | --------------------------- 164 | - Loading references from data/names/gold/ru.json... 165 | - Loading predictions from data/names/m-nta/with_gpt-3/ru.m-nta.json... 166 | # of entities: 1,000 167 | # of entities with correct names: 1,000 168 | # of entities with incorrect names: 1,000 169 | # of entities in the predictions: 923 170 | --------------------------- 171 | Total incorrect names: 614 172 | Identified incorrect names: 483 173 | Total correct names: 3,287 174 | Correct names classified as incorrect: 1,522 175 | --------------------------- 176 | Precision - P = 57.6 177 | Precision - R = 78.7 178 | Precision - F1 = 66.5 179 | =========================== 180 | 181 | =========================== 182 | Starting evaluation... 183 | --------------------------- 184 | - Loading references from data/names/gold/zh.json... 185 | - Loading predictions from data/names/m-nta/with_gpt-3/zh.m-nta.json... 186 | # of entities: 1,000 187 | # of entities with correct names: 1,000 188 | # of entities with incorrect names: 1,000 189 | # of entities in the predictions: 896 190 | --------------------------- 191 | Total incorrect names: 811 192 | Identified incorrect names: 742 193 | Total correct names: 3,277 194 | Correct names classified as incorrect: 1,702 195 | --------------------------- 196 | Precision - P = 56.7 197 | Precision - R = 91.5 198 | Precision - F1 = 70.0 199 | =========================== 200 | 201 | -------------------------------------------------------------------------------- /results/precision/m-nta.gpt-3.5.out: -------------------------------------------------------------------------------- 1 | =========================== 2 | Starting evaluation... 3 | --------------------------- 4 | - Loading references from data/names/gold/ar.json... 5 | - Loading predictions from data/names/m-nta/with_gpt-3.5/ar.m-nta.json... 6 | # of entities: 1,000 7 | # of entities with correct names: 1,000 8 | # of entities with incorrect names: 1,000 9 | # of entities in the predictions: 869 10 | --------------------------- 11 | Total incorrect names: 320 12 | Identified incorrect names: 283 13 | Total correct names: 3,893 14 | Correct names classified as incorrect: 1,474 15 | --------------------------- 16 | Precision - P = 64.1 17 | Precision - R = 88.4 18 | Precision - F1 = 74.4 19 | =========================== 20 | 21 | =========================== 22 | Starting evaluation... 23 | --------------------------- 24 | - Loading references from data/names/gold/de.json... 25 | - Loading predictions from data/names/m-nta/with_gpt-3.5/de.m-nta.json... 26 | # of entities: 1,000 27 | # of entities with correct names: 999 28 | # of entities with incorrect names: 999 29 | # of entities in the predictions: 939 30 | --------------------------- 31 | Total incorrect names: 491 32 | Identified incorrect names: 408 33 | Total correct names: 3,007 34 | Correct names classified as incorrect: 870 35 | --------------------------- 36 | Precision - P = 72.8 37 | Precision - R = 83.1 38 | Precision - F1 = 77.6 39 | =========================== 40 | 41 | =========================== 42 | Starting evaluation... 43 | --------------------------- 44 | - Loading references from data/names/gold/en.json... 45 | - Loading predictions from data/names/m-nta/with_gpt-3.5/en.m-nta.json... 46 | # of entities: 1,000 47 | # of entities with correct names: 1,000 48 | # of entities with incorrect names: 1,000 49 | # of entities in the predictions: 776 50 | --------------------------- 51 | Total incorrect names: 219 52 | Identified incorrect names: 175 53 | Total correct names: 2,618 54 | Correct names classified as incorrect: 1,246 55 | --------------------------- 56 | Precision - P = 54.5 57 | Precision - R = 79.9 58 | Precision - F1 = 64.8 59 | =========================== 60 | 61 | =========================== 62 | Starting evaluation... 63 | --------------------------- 64 | - Loading references from data/names/gold/es.json... 65 | - Loading predictions from data/names/m-nta/with_gpt-3.5/es.m-nta.json... 66 | # of entities: 1,000 67 | # of entities with correct names: 1,000 68 | # of entities with incorrect names: 1,000 69 | # of entities in the predictions: 951 70 | --------------------------- 71 | Total incorrect names: 571 72 | Identified incorrect names: 447 73 | Total correct names: 3,749 74 | Correct names classified as incorrect: 1,089 75 | --------------------------- 76 | Precision - P = 71.9 77 | Precision - R = 78.3 78 | Precision - F1 = 75.0 79 | =========================== 80 | 81 | =========================== 82 | Starting evaluation... 83 | --------------------------- 84 | - Loading references from data/names/gold/fr.json... 85 | - Loading predictions from data/names/m-nta/with_gpt-3.5/fr.m-nta.json... 86 | # of entities: 1,000 87 | # of entities with correct names: 1,000 88 | # of entities with incorrect names: 1,000 89 | # of entities in the predictions: 946 90 | --------------------------- 91 | Total incorrect names: 533 92 | Identified incorrect names: 366 93 | Total correct names: 3,015 94 | Correct names classified as incorrect: 846 95 | --------------------------- 96 | Precision - P = 71.4 97 | Precision - R = 68.7 98 | Precision - F1 = 70.0 99 | =========================== 100 | 101 | =========================== 102 | Starting evaluation... 103 | --------------------------- 104 | - Loading references from data/names/gold/it.json... 105 | - Loading predictions from data/names/m-nta/with_gpt-3.5/it.m-nta.json... 106 | # of entities: 999 107 | # of entities with correct names: 999 108 | # of entities with incorrect names: 999 109 | # of entities in the predictions: 952 110 | --------------------------- 111 | Total incorrect names: 267 112 | Identified incorrect names: 192 113 | Total correct names: 2,889 114 | Correct names classified as incorrect: 592 115 | --------------------------- 116 | Precision - P = 78.9 117 | Precision - R = 71.9 118 | Precision - F1 = 75.2 119 | =========================== 120 | 121 | =========================== 122 | Starting evaluation... 123 | --------------------------- 124 | - Loading references from data/names/gold/ja.json... 125 | - Loading predictions from data/names/m-nta/with_gpt-3.5/ja.m-nta.json... 126 | # of entities: 1,000 127 | # of entities with correct names: 1,000 128 | # of entities with incorrect names: 1,000 129 | # of entities in the predictions: 829 130 | --------------------------- 131 | Total incorrect names: 507 132 | Identified incorrect names: 464 133 | Total correct names: 2,492 134 | Correct names classified as incorrect: 1,372 135 | --------------------------- 136 | Precision - P = 52.8 137 | Precision - R = 91.5 138 | Precision - F1 = 67.0 139 | =========================== 140 | 141 | =========================== 142 | Starting evaluation... 143 | --------------------------- 144 | - Loading references from data/names/gold/ko.json... 145 | - Loading predictions from data/names/m-nta/with_gpt-3.5/ko.m-nta.json... 146 | # of entities: 1,000 147 | # of entities with correct names: 1,000 148 | # of entities with incorrect names: 1,000 149 | # of entities in the predictions: 874 150 | --------------------------- 151 | Total incorrect names: 330 152 | Identified incorrect names: 268 153 | Total correct names: 3,544 154 | Correct names classified as incorrect: 995 155 | --------------------------- 156 | Precision - P = 72.7 157 | Precision - R = 81.2 158 | Precision - F1 = 76.7 159 | =========================== 160 | 161 | =========================== 162 | Starting evaluation... 163 | --------------------------- 164 | - Loading references from data/names/gold/ru.json... 165 | - Loading predictions from data/names/m-nta/with_gpt-3.5/ru.m-nta.json... 166 | # of entities: 1,000 167 | # of entities with correct names: 1,000 168 | # of entities with incorrect names: 1,000 169 | # of entities in the predictions: 923 170 | --------------------------- 171 | Total incorrect names: 614 172 | Identified incorrect names: 480 173 | Total correct names: 3,287 174 | Correct names classified as incorrect: 1,488 175 | --------------------------- 176 | Precision - P = 58.4 177 | Precision - R = 78.2 178 | Precision - F1 = 66.9 179 | =========================== 180 | 181 | =========================== 182 | Starting evaluation... 183 | --------------------------- 184 | - Loading references from data/names/gold/zh.json... 185 | - Loading predictions from data/names/m-nta/with_gpt-3.5/zh.m-nta.json... 186 | # of entities: 1,000 187 | # of entities with correct names: 1,000 188 | # of entities with incorrect names: 1,000 189 | # of entities in the predictions: 896 190 | --------------------------- 191 | Total incorrect names: 811 192 | Identified incorrect names: 752 193 | Total correct names: 3,277 194 | Correct names classified as incorrect: 1,656 195 | --------------------------- 196 | Precision - P = 58.0 197 | Precision - R = 92.7 198 | Precision - F1 = 71.4 199 | =========================== 200 | 201 | -------------------------------------------------------------------------------- /src/evaluation/evaluate_coverage.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/python3 2 | 3 | """Evaluate a set of translations against a set of references. 4 | 5 | Reads the references contained in --references_path and the 6 | translations contained in --predictions_path and performs an 7 | evaluation of the translation using a scorer specified by --scorer. 8 | 9 | Typical usage example: 10 | 11 | python src/evaluation/evaluate_translations.py \ 12 | --references_path references.json \ 13 | --predictions_path predictions.json 14 | 15 | """ 16 | 17 | import argparse 18 | import json 19 | from typing import Dict, List 20 | 21 | 22 | def normalize_value(value: str) -> str: 23 | value = value.lower() 24 | value = "".join([i for i in value if i.isalnum() or i == " "]) 25 | return value 26 | 27 | 28 | def read_references(input_path: str) -> Dict[str, List[str]]: 29 | data: Dict[int, List[str]] = {} 30 | 31 | with open(input_path) as f: 32 | for line in f: 33 | line = line.strip() 34 | if not line: 35 | continue 36 | 37 | line_data = json.loads(line) 38 | wikidata_id = line_data["wikidata_id"] 39 | values = line_data["correct_values"] 40 | values = [normalize_value(v) for v in values] 41 | values = set(values) 42 | 43 | if values: 44 | data[wikidata_id] = values 45 | 46 | return data 47 | 48 | 49 | def read_predictions(input_path: str, threshold: int) -> Dict[str, List[str]]: 50 | data: Dict[int, List[str]] = {} 51 | 52 | with open(input_path) as f: 53 | for line in f: 54 | line = line.strip() 55 | if not line: 56 | continue 57 | 58 | line_data = json.loads(line) 59 | wikidata_id = line_data["wikidata_id"] 60 | values = line_data["values"] 61 | values = [normalize_value(v) for s, v, *_ in values if s >= threshold] 62 | values = set(values) 63 | 64 | if values: 65 | data[wikidata_id] = values 66 | 67 | return data 68 | 69 | 70 | def compute_micro_f1( 71 | precision_scores: List[List[float]], 72 | recall_scores: List[List[float]], 73 | ): 74 | precision_num = sum([sum(scores) for scores in precision_scores]) 75 | precision_den = sum([len(scores) for scores in precision_scores]) 76 | precision = precision_num / precision_den 77 | 78 | recall_num = sum([sum(scores) for scores in recall_scores]) 79 | recall_den = sum([len(scores) for scores in recall_scores]) 80 | recall = recall_num / recall_den 81 | 82 | f1 = 2 * precision * recall / (precision + recall) 83 | 84 | return precision, recall, f1 85 | 86 | 87 | def compute_macro_f1( 88 | precision_scores: List[List[float]], 89 | recall_scores: List[List[float]], 90 | ): 91 | precision_scores = [sum(scores) / len(scores) for scores in precision_scores] 92 | recall_scores = [sum(scores) / len(scores) for scores in recall_scores] 93 | 94 | precision = sum(precision_scores) / len(precision_scores) 95 | recall = sum(recall_scores) / len(recall_scores) 96 | 97 | f1 = 2 * precision * recall / (precision + recall) 98 | 99 | return precision, recall, f1 100 | 101 | 102 | if __name__ == "__main__": 103 | parser = argparse.ArgumentParser() 104 | 105 | parser.add_argument( 106 | "--references_path", 107 | type=str, 108 | required=True, 109 | help="Path to the JSONL file containing the references.", 110 | ) 111 | parser.add_argument( 112 | "--predictions_path", 113 | type=str, 114 | required=True, 115 | help="Path to the JSONL file containing the predictions.", 116 | ) 117 | parser.add_argument( 118 | "--wikidata_ids_path", 119 | type=str, 120 | required=False, 121 | help="Allows the evaluation to be performed only on the subset of entities specified in this .txt file.", 122 | ) 123 | parser.add_argument( 124 | "--print_pairwise_scores", 125 | action="store_true", 126 | help="If set, prints reference-prediction pairs.", 127 | ) 128 | parser.add_argument( 129 | "--quiet", 130 | action="store_true", 131 | help="Use this flag to reduce the verbosity of the output.", 132 | ) 133 | parser.add_argument( 134 | "--score_threshold", 135 | type=int, 136 | default=0, 137 | help="Minimum score for the predictions to be evaluated.", 138 | ) 139 | 140 | args = parser.parse_args() 141 | 142 | print("===========================") 143 | print("Starting evaluation...") 144 | print("---------------------------") 145 | 146 | # Load references. 147 | print(f"- Loading references from {args.references_path}...") 148 | references = read_references(args.references_path) 149 | 150 | # Load predictions. 151 | print(f"- Loading predictions from {args.predictions_path}...") 152 | predictions = read_predictions(args.predictions_path, args.score_threshold) 153 | 154 | # Read Wikidata IDs if provided. 155 | if args.wikidata_ids_path: 156 | wikidata_ids = set() 157 | 158 | with open(args.wikidata_ids_path) as f: 159 | for line in f: 160 | line = line.strip() 161 | if not line: 162 | continue 163 | 164 | wikidata_id = line.strip() 165 | wikidata_ids.add(wikidata_id) 166 | 167 | precision_scores = [] 168 | recall_scores = [] 169 | 170 | # Compute precision scores for all instances in the predictions. 171 | for id, instance_predictions in predictions.items(): 172 | if args.wikidata_ids_path and id not in wikidata_ids: 173 | continue 174 | 175 | if id not in references: 176 | continue 177 | 178 | instance_references = references[id] 179 | instance_scores = [] 180 | 181 | for prediction in instance_predictions: 182 | if prediction in instance_references: 183 | score = 1.0 184 | else: 185 | score = 0.0 186 | instance_scores.append(score) 187 | 188 | precision_scores.append(instance_scores) 189 | 190 | # If arg.print_pairwise_scores is set, print the scores for each entity. 191 | if args.print_pairwise_scores: 192 | print(id) 193 | print(" Gold:", instance_references) 194 | print(" Pred:", instance_predictions) 195 | _score = sum(instance_scores) / len(instance_scores) 196 | print(f" Precision: {_score}") 197 | print() 198 | 199 | # Compute recall scores for all instances in the references. 200 | for id, instance_references in references.items(): 201 | if args.wikidata_ids_path and id not in wikidata_ids: 202 | continue 203 | 204 | instance_predictions = predictions.get(id, []) 205 | 206 | instance_scores = [] 207 | for reference in instance_references: 208 | if reference in instance_predictions: 209 | score = 1.0 210 | else: 211 | score = 0.0 212 | instance_scores.append(score) 213 | 214 | recall_scores.append(instance_scores) 215 | 216 | # If arg.print_pairwise_scores is set, print the scores for each entity. 217 | if args.print_pairwise_scores: 218 | print(id) 219 | print(" Gold:", instance_references) 220 | print(" Pred:", instance_predictions) 221 | _score = sum(instance_scores) / len(instance_scores) 222 | print(f" Recall: {_score}") 223 | print() 224 | 225 | if not args.quiet: 226 | print(f" # of entities in the references: {len(references):,}") 227 | print(f" # of entities in the predictions: {len(predictions):,}") 228 | print("---------------------------") 229 | 230 | precision, recall, f1 = compute_micro_f1(precision_scores, recall_scores) 231 | 232 | if not args.quiet: 233 | print(f" Coverage - P = {100.*precision:0.1f}") 234 | print(f" Coverage - R = {100.*recall:0.1f}") 235 | print(f" Coverage - F1 = {100.*f1:0.1f}") 236 | print("===========================") 237 | print() 238 | 239 | if args.quiet: 240 | print(f"{100.*precision:0.2f} {100.*recall:0.2f} {100.*f1:0.2f}") 241 | -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | 2 | 3 | Apache License 4 | Version 2.0, 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Coverage 27 | Suppose we want to add support to Wikidata for entity names (or other types of textual information, e.g., entity descriptions) in a new target language `l_t`. 28 | *Coverage* measures the ability of an automatic approach to provide at least a valid entity name in `l_t` for each entity of interest in Wikidata. 29 | 30 | In other words, measuring *Coverage* is equivalent to answering the following question: How effective is an automatic approach in converting the entity names from a source language `l_s` to a target language `l_t`? 31 | For example, how can we use the English entity names to create valid Japanese entity names with the same quantity and quality of the English ones? 32 | 33 | ### MKGE - Precision 34 | It is well-known that the quality of the information in Wikidata is not perfect. 35 | *Precision* measures the ability of an automatic approach to identify incorrect entity names (or other types of textual information, e.g., entity descriptions) for an entity of interest in a target language `l_t`. 36 | 37 | In other words, measuring *Precision* is equivalent to answering the following question: How effective is an automatic approach in recognizing noisy, incomplete, or outdated information in a target language `l_t`? 38 | 39 | 40 | ## WikiKGE-10 41 | WikiKGE-10 is a benchmark for evaluating automatic approaches for increasing both **coverage** and **precision** of entity names in Wikidata for 10 languages. 42 | 43 | WikiKGE-10 includes around 1000 entities in each of the following 10 languages: 44 | * `ar` - Arabic 45 | * `de` - German 46 | * `en` - English 47 | * `es` - Spanish 48 | * `fr` - French 49 | * `it` - Italian 50 | * `ja` - Japanese 51 | * `ko` - Korean 52 | * `ru` - Russian 53 | * `zh` - Simplified Chinese 54 | 55 | ### Dataset organization 56 | The data is organized in the following way: 57 | ``` 58 | data 59 | └── names 60 | ├── gold 61 | │ ├── ar.json 62 | │ ├── de.json 63 | ... ... 64 | ├── m-nta 65 | │ ├── with_gpt-3 66 | │ │ ├── ar.m-nta.json 67 | │ │ ├── de.m-nta.json 68 | ... ... ... 69 | │ ├── with_gpt-3.5 70 | │ │ ├── ar.m-nta.json 71 | │ │ ├── de.m-nta.json 72 | ... ... ... 73 | │ └── with_gpt-4 74 | │ ├── ar.m-nta.json 75 | │ ├── de.m-nta.json 76 | ... ... ... 77 | └── gpt 78 | │ ├── ar.gpt-3.json 79 | │ ├── de.gpt-3.json 80 | ... ... 81 | 82 | └── wikidata 83 | ├── ar.json 84 | ├── de.json 85 | ... 86 | └── zh.json 87 | ``` 88 | Where: 89 | * `data/names/gold/` contains the human-curated data. 90 | * `data/names/m-nta/` contains the predictions from M-NTA. 91 | * `data/names/gpt/` contains the predictions from GPT-3 and GPT-3.5 (May 2023), and also GPT-4 (September 2023). 92 | * `data/names/wikidata/` contains the data from Wikidata (May 2023). 93 | 94 | 95 | ### Human-curated data in WikiKGE-10 96 | Here are a few examples in `data/names/gold/it.json`: 97 | ```json 98 | { 99 | "wikidata_id": "Q48324", 100 | "correct_values": ["morale", "moralità", "Moralismo"], 101 | "incorrect_values": ["giudizio morale", "moralita'", "legge morale"] 102 | } 103 | ``` 104 | ```json 105 | { 106 | "wikidata_id": "Q166844", 107 | "correct_values": ["Thomas N'Kono", "N'Kono"], 108 | "incorrect_values": ["Thomas Nkono"] 109 | } 110 | 111 | ``` 112 | 113 | Where: 114 | * `wikidata_id` is the QID of the entity in Wikidata. 115 | * `correct_values` is a list of entity names that have been rated as valid by our human annotators. 116 | * `incorrect_values` is a list of entity names that are in Wikidata but have been rated as invalid by our human annotators. 117 | 118 | 119 | ### M-NTA predictions in WikiKGE-10 120 | We also include the entity names predicted by M-NTA, our automatic system for MKGE, to reproduces the results of our experiments. 121 | 122 | Here are a few examples of the predictions found in `data/names/m-nta/no_gpt/it.json`: 123 | ```json 124 | { 125 | "wikidata_id": "Q48324", 126 | "values": [ 127 | [1, "Egenetica", false], 128 | [1, "Immorale", false], 129 | [1, "Immoralità", false], 130 | [1, "Morali", false], 131 | [1, "Moralismo", false], 132 | [1, "Moralità pubblica", false], 133 | [1, "Moralmente", false], 134 | [1, "Parenesi", false], 135 | [1, "Pubblica moralità", false], 136 | [1, "Regola morale", false], 137 | [1, "Teoria dei costumi", false], 138 | [4, "Morale", true], 139 | [4, "Moralità", true] 140 | ] 141 | } 142 | ``` 143 | ```json 144 | { 145 | "wikidata_id": "Q166844", 146 | "values": [ 147 | [1, "Thomas 'Tommy' N'Kono", false], 148 | [1, "Thomas Nucono", true], 149 | [1, "Tommy N'Kono", false], 150 | [3, "N'Kono", false], 151 | [3, "Nkono", false], 152 | [6, "Thomas N'Kono", true], 153 | [6, "Thomas NKono", false], 154 | [6, "Thomas Nkono", false] 155 | ] 156 | } 157 | ``` 158 | Where: 159 | * `wikidata_id` is the QID of the entity in Wikidata. 160 | * `values` is a list of predictions from M-NTA: 161 | * `value[0]` is the confidence score from M-NTA 162 | * `value[1]` is the prediction from M-NTA 163 | * `value[2]` is whether the prediction comes from a Wikidata primary name. 164 | 165 | 166 | ## Evaluation 167 | To evaluate the **coverage** of a system, you can run the following command: 168 | ```bash 169 | python src/evaluation/evaluate_coverage.py \ 170 | --references_path data/names/gold/es.json \ 171 | --predictions_path data/names/m-nta/es.json \ 172 | --score_threshold 2 173 | ``` 174 | You will see the following output: 175 | ``` 176 | =========================== 177 | Starting evaluation... 178 | --------------------------- 179 | - Loading references from data/names/gold/es.json... 180 | - Loading predictions from data/names/m-nta/with_gpt-3.5/es.m-nta.json... 181 | # of entities in the references: 1000 182 | # of entities in the predictions: 889 183 | --------------------------- 184 | Coverage - P = 77.2 185 | Coverage - R = 43.4 186 | Coverage - F1 = 55.6 187 | =========================== 188 | ``` 189 | 190 | To evaluate the **precision** of a system, you can run the following command: 191 | ```bash 192 | python src/evaluation/evaluate_precision.py \ 193 | --references_path data/names/gold/es.json \ 194 | --predictions_path data/names/m-nta/es.json \ 195 | --score_threshold 1 196 | ``` 197 | You will see the following output: 198 | ``` 199 | =========================== 200 | Starting evaluation... 201 | --------------------------- 202 | - Loading references from data/names/gold/es.json... 203 | - Loading predictions from data/names/m-nta/with_gpt-3.5/es.m-nta.json... 204 | # of entities: 1,000 205 | # of entities with correct names: 1,000 206 | # of entities with incorrect names: 1,000 207 | # of entities in the predictions: 951 208 | --------------------------- 209 | Total incorrect names: 571 210 | Identified incorrect names: 447 211 | Total correct names: 3,749 212 | Correct names classified as incorrect: 1,089 213 | --------------------------- 214 | Precision - P = 71.9 215 | Precision - R = 78.3 216 | Precision - F1 = 75.0 217 | =========================== 218 | ``` 219 | 220 | ### Results 221 | The main results from the paper can be found in `results/coverage` and `results/precision`. 222 | 223 | ## Citation 224 | Please cite our work if you found WikiKGE-10, our [paper](), or these resources useful. 225 | 226 | ```bibtex 227 | @inproceedings{conia-etal-2023-increasing, 228 | title = "Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs", 229 | author = "Conia, Simone and 230 | Li, Min and 231 | Lee, Daniel and 232 | Minhas, Umar Farooq and 233 | Ilyas, Ihab and 234 | Li, Yunyao", 235 | booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023)", 236 | month = dec, 237 | year = "2023", 238 | address = "Singapore", 239 | publisher = "Association for Computational Linguistics", 240 | } 241 | ``` 242 | 243 | ## License 244 | The code in this repository is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), see the [LICENSE.txt](LICENSE.txt) file. 245 | 246 | WikiKGE-10 is licensed under [CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/deed.en), see the [LICENSE_Wikidata.txt](LICENSE_Wikidata.txt) file. 247 | 248 | ## Acknowledgements 249 | This work is part of one of the projects I carried out during my internship at Apple. 250 | I must truly thank Min Li and Yunyao Li for their incredible mentorship and for everything they taught me. 251 | I would also like to thank Umar Farooq Minhas, Saloni Potdar, and Ihab Ilyas for their valuable feedback. 252 | My gratitude also goes to Behrang Mohit for his insightful comments on the paper. 253 | Besides his technical contributions, I would like to thank Daniel Lee for making this project more fun, and Farima Fatahi Bayat, Ronak Pradeep, and Revanth Gangi Reddy for making this internship a unique experience. 254 | -------------------------------------------------------------------------------- /LICENSE_Wikidata.txt: -------------------------------------------------------------------------------- 1 | Attribution-ShareAlike 4.0 International 2 | 3 | ======================================================================= 4 | 5 | Creative Commons Corporation ("Creative Commons") is not a law firm and 6 | does not provide legal services or legal advice. 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For 424 | the avoidance of doubt, this paragraph does not form part of the 425 | public licenses. 426 | 427 | Creative Commons may be contacted at creativecommons.org. 428 | -------------------------------------------------------------------------------- /data/names/gpt/ja.gpt-4.json: -------------------------------------------------------------------------------- 1 | {"wikidata_id": "Q1000043", "values": [[1, "若島津健", true]]} 2 | {"wikidata_id": "Q1000658", "values": [[1, "保障に関するブダペスト覚書", true], [1, "ブダペスト覚書", false]]} 3 | {"wikidata_id": "Q1005361", "values": [[1, "マリと三匹の子犬の物語", true], [1, "マリと三匹の子犬の物語", false]]} 4 | {"wikidata_id": "Q1007", "values": [[1, "ギニアビサウ", true], [1, "GBS", false], [1, "🇬🇼", false], [1, "gw", false], [1, "ギニアビサウ共和国", false]]} 5 | {"wikidata_id": "Q1011157", "values": [[1, "白馬", true]]} 6 | {"wikidata_id": "Q101228962", "values": [[1, "吉田雪子", true]]} 7 | {"wikidata_id": "Q1012807", "values": [[1, "ラムタラ", true]]} 8 | {"wikidata_id": "Q102071", "values": [[1, 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