{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:WOL2G2A3ZXW2JDW2SGDYURXNIY","short_pith_number":"pith:WOL2G2A3","schema_version":"1.0","canonical_sha256":"b397a3681bcdeda48eda91878a46ed460cf0130636018e893eb45e221d252d30","source":{"kind":"arxiv","id":"2411.04473","version":1},"attestation_state":"computed","paper":{"title":"ML-Promise: A Multilingual Dataset for Corporate Promise Verification","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ana\\\"is Lhuissier, Chung-Chi Chen, Hakusen Shu, Hanwool Lee, Juyeon Kang, Min-Yuh Day, Yohei Seki","submitted_at":"2024-11-07T06:51:24Z","abstract_excerpt":"Promises made by politicians, corporate leaders, and public figures have a significant impact on public perception, trust, and institutional reputation. However, the complexity and volume of such commitments, coupled with difficulties in verifying their fulfillment, necessitate innovative methods for assessing their credibility. This paper introduces the concept of Promise Verification, a systematic approach involving steps such as promise identification, evidence assessment, and the evaluation of timing for verification. We propose the first multilingual dataset, ML-Promise, which includes En"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.04473","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-07T06:51:24Z","cross_cats_sorted":[],"title_canon_sha256":"dc3f8bbe7d5364c2a3dea25bea373f6d7b2648d569f2f29d2dca8cef3c7bc4b4","abstract_canon_sha256":"ef42bbd7dc259b5e1795c0d40c46197f4c594314a889c9214859b065a4015c74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:20.018192Z","signature_b64":"LMAYQh+2Zu4R+/jv246s9Ip6372TSJTgSBb6JuaAi1J0fexXF2RaxJ2zETxGqATQuLA+dx8kZxN/7CA7BG/qAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b397a3681bcdeda48eda91878a46ed460cf0130636018e893eb45e221d252d30","last_reissued_at":"2026-07-05T09:32:20.017701Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:20.017701Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ML-Promise: A Multilingual Dataset for Corporate Promise Verification","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ana\\\"is Lhuissier, Chung-Chi Chen, Hakusen Shu, Hanwool Lee, Juyeon Kang, Min-Yuh Day, Yohei Seki","submitted_at":"2024-11-07T06:51:24Z","abstract_excerpt":"Promises made by politicians, corporate leaders, and public figures have a significant impact on public perception, trust, and institutional reputation. However, the complexity and volume of such commitments, coupled with difficulties in verifying their fulfillment, necessitate innovative methods for assessing their credibility. This paper introduces the concept of Promise Verification, a systematic approach involving steps such as promise identification, evidence assessment, and the evaluation of timing for verification. We propose the first multilingual dataset, ML-Promise, which includes En"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04473","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.04473/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.04473","created_at":"2026-07-05T09:32:20.017758+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.04473v1","created_at":"2026-07-05T09:32:20.017758+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04473","created_at":"2026-07-05T09:32:20.017758+00:00"},{"alias_kind":"pith_short_12","alias_value":"WOL2G2A3ZXW2","created_at":"2026-07-05T09:32:20.017758+00:00"},{"alias_kind":"pith_short_16","alias_value":"WOL2G2A3ZXW2JDW2","created_at":"2026-07-05T09:32:20.017758+00:00"},{"alias_kind":"pith_short_8","alias_value":"WOL2G2A3","created_at":"2026-07-05T09:32:20.017758+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.23538","citing_title":"CLaC at SemEval-2025 Task 6: A Multi-Architecture Approach for Corporate Environmental Promise Verification","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WOL2G2A3ZXW2JDW2SGDYURXNIY","json":"https://pith.science/pith/WOL2G2A3ZXW2JDW2SGDYURXNIY.json","graph_json":"https://pith.science/api/pith-number/WOL2G2A3ZXW2JDW2SGDYURXNIY/graph.json","events_json":"https://pith.science/api/pith-number/WOL2G2A3ZXW2JDW2SGDYURXNIY/events.json","paper":"https://pith.science/paper/WOL2G2A3"},"agent_actions":{"view_html":"https://pith.science/pith/WOL2G2A3ZXW2JDW2SGDYURXNIY","download_json":"https://pith.science/pith/WOL2G2A3ZXW2JDW2SGDYURXNIY.json","view_paper":"https://pith.science/paper/WOL2G2A3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.04473&json=true","fetch_graph":"https://pith.science/api/pith-number/WOL2G2A3ZXW2JDW2SGDYURXNIY/graph.json","fetch_events":"https://pith.science/api/pith-number/WOL2G2A3ZXW2JDW2SGDYURXNIY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WOL2G2A3ZXW2JDW2SGDYURXNIY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WOL2G2A3ZXW2JDW2SGDYURXNIY/action/storage_attestation","attest_author":"https://pith.science/pith/WOL2G2A3ZXW2JDW2SGDYURXNIY/action/author_attestation","sign_citation":"https://pith.science/pith/WOL2G2A3ZXW2JDW2SGDYURXNIY/action/citation_signature","submit_replication":"https://pith.science/pith/WOL2G2A3ZXW2JDW2SGDYURXNIY/action/replication_record"}},"created_at":"2026-07-05T09:32:20.017758+00:00","updated_at":"2026-07-05T09:32:20.017758+00:00"}