{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:36O4N7SA7XYJCEZIG76TBIGAY4","short_pith_number":"pith:36O4N7SA","canonical_record":{"source":{"id":"2405.16806","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T03:52:55Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e7c2933ea13b5646e9e9837c6717bc698fd9f1e2056fa2959531864dd0407f89","abstract_canon_sha256":"64aee98ef89334c3cf1afce74da16d8f3ba7958e68688df2d63a74ca3d7e37bf"},"schema_version":"1.0"},"canonical_sha256":"df9dc6fe40fdf091132837fd30a0c0c7216f03b2560fdc5a3bef915d5f925504","source":{"kind":"arxiv","id":"2405.16806","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.16806","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"arxiv_version","alias_value":"2405.16806v2","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.16806","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"pith_short_12","alias_value":"36O4N7SA7XYJ","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"pith_short_16","alias_value":"36O4N7SA7XYJCEZI","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"pith_short_8","alias_value":"36O4N7SA","created_at":"2026-07-05T10:11:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:36O4N7SA7XYJCEZIG76TBIGAY4","target":"record","payload":{"canonical_record":{"source":{"id":"2405.16806","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T03:52:55Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e7c2933ea13b5646e9e9837c6717bc698fd9f1e2056fa2959531864dd0407f89","abstract_canon_sha256":"64aee98ef89334c3cf1afce74da16d8f3ba7958e68688df2d63a74ca3d7e37bf"},"schema_version":"1.0"},"canonical_sha256":"df9dc6fe40fdf091132837fd30a0c0c7216f03b2560fdc5a3bef915d5f925504","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:20.016972Z","signature_b64":"JL776kqbnbVeuPXRO98Waz3WWQIwFOEY2jmMr71Gvyh/FhbwFdjtK57Ga8/Qsvr1L/l3KhQlvxDUgHcGqyLBDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df9dc6fe40fdf091132837fd30a0c0c7216f03b2560fdc5a3bef915d5f925504","last_reissued_at":"2026-07-05T10:11:20.016478Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:20.016478Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.16806","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:11:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bglqa0VOJungmyGaN9cYEpr+ziPqyrKC3xV2opMCWhbFtoDUEIhNN3q8dvA2jBsp/fi/ZVsJlD7/B04tXxY3CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:54:17.280914Z"},"content_sha256":"e203fda5c5a389ae99cc86b6a7b355a5f444fb72a21d48ef83955b1e80738110","schema_version":"1.0","event_id":"sha256:e203fda5c5a389ae99cc86b6a7b355a5f444fb72a21d48ef83955b1e80738110"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:36O4N7SA7XYJCEZIG76TBIGAY4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Entity Alignment with Noisy Annotations from Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Junnan Dong, Qinggang Zhang, Qing Li, Shengyuan Chen, Wen Hua, Xiao Huang","submitted_at":"2024-05-27T03:52:55Z","abstract_excerpt":"Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. While existing methods heavily rely on human-generated labels, it is prohibitively expensive to incorporate cross-domain experts for annotation in real-world scenarios. The advent of Large Language Models (LLMs) presents new avenues for automating EA with annotations, inspired by their comprehensive capability to process semantic information. However, it is nontrivial to directly apply LLMs for EA since the annotation space in real-world KGs is large. LLMs could also generate noisy labels tha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.16806","kind":"arxiv","version":2},"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/2405.16806/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:11:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y6Sz17P9i1PPCbEPsvzzYjfqgu86H1c8dxrpaWbxY1QSMMv9apE7RhofqzirWA6M0bmqPWDzO25mOK5oqYbWAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:54:17.281403Z"},"content_sha256":"e759047d304035ac5a2d4e840ef5e5d6478d670e4b97f97a3befe220bf1c888b","schema_version":"1.0","event_id":"sha256:e759047d304035ac5a2d4e840ef5e5d6478d670e4b97f97a3befe220bf1c888b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/36O4N7SA7XYJCEZIG76TBIGAY4/bundle.json","state_url":"https://pith.science/pith/36O4N7SA7XYJCEZIG76TBIGAY4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/36O4N7SA7XYJCEZIG76TBIGAY4/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T11:54:17Z","links":{"resolver":"https://pith.science/pith/36O4N7SA7XYJCEZIG76TBIGAY4","bundle":"https://pith.science/pith/36O4N7SA7XYJCEZIG76TBIGAY4/bundle.json","state":"https://pith.science/pith/36O4N7SA7XYJCEZIG76TBIGAY4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/36O4N7SA7XYJCEZIG76TBIGAY4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:36O4N7SA7XYJCEZIG76TBIGAY4","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"64aee98ef89334c3cf1afce74da16d8f3ba7958e68688df2d63a74ca3d7e37bf","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T03:52:55Z","title_canon_sha256":"e7c2933ea13b5646e9e9837c6717bc698fd9f1e2056fa2959531864dd0407f89"},"schema_version":"1.0","source":{"id":"2405.16806","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.16806","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"arxiv_version","alias_value":"2405.16806v2","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.16806","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"pith_short_12","alias_value":"36O4N7SA7XYJ","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"pith_short_16","alias_value":"36O4N7SA7XYJCEZI","created_at":"2026-07-05T10:11:20Z"},{"alias_kind":"pith_short_8","alias_value":"36O4N7SA","created_at":"2026-07-05T10:11:20Z"}],"graph_snapshots":[{"event_id":"sha256:e759047d304035ac5a2d4e840ef5e5d6478d670e4b97f97a3befe220bf1c888b","target":"graph","created_at":"2026-07-05T10:11:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2405.16806/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. While existing methods heavily rely on human-generated labels, it is prohibitively expensive to incorporate cross-domain experts for annotation in real-world scenarios. The advent of Large Language Models (LLMs) presents new avenues for automating EA with annotations, inspired by their comprehensive capability to process semantic information. However, it is nontrivial to directly apply LLMs for EA since the annotation space in real-world KGs is large. LLMs could also generate noisy labels tha","authors_text":"Junnan Dong, Qinggang Zhang, Qing Li, Shengyuan Chen, Wen Hua, Xiao Huang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T03:52:55Z","title":"Entity Alignment with Noisy Annotations from Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.16806","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:e203fda5c5a389ae99cc86b6a7b355a5f444fb72a21d48ef83955b1e80738110","target":"record","created_at":"2026-07-05T10:11:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"64aee98ef89334c3cf1afce74da16d8f3ba7958e68688df2d63a74ca3d7e37bf","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-27T03:52:55Z","title_canon_sha256":"e7c2933ea13b5646e9e9837c6717bc698fd9f1e2056fa2959531864dd0407f89"},"schema_version":"1.0","source":{"id":"2405.16806","kind":"arxiv","version":2}},"canonical_sha256":"df9dc6fe40fdf091132837fd30a0c0c7216f03b2560fdc5a3bef915d5f925504","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df9dc6fe40fdf091132837fd30a0c0c7216f03b2560fdc5a3bef915d5f925504","first_computed_at":"2026-07-05T10:11:20.016478Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:11:20.016478Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JL776kqbnbVeuPXRO98Waz3WWQIwFOEY2jmMr71Gvyh/FhbwFdjtK57Ga8/Qsvr1L/l3KhQlvxDUgHcGqyLBDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:11:20.016972Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.16806","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e203fda5c5a389ae99cc86b6a7b355a5f444fb72a21d48ef83955b1e80738110","sha256:e759047d304035ac5a2d4e840ef5e5d6478d670e4b97f97a3befe220bf1c888b"],"state_sha256":"6047da53d32bc0514771eb139b804fa1ab29fcd6ede5da26e3fccb541ae1ba01"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vTxOfDiW5cbV5nIXfctg1PXgg5IIs6ilUzV/uK+3k3qQtqm+Rg/FnqYJ2LJPdM8bqBW9UG+Y8Vf6A2UEPr+zAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T11:54:17.285117Z","bundle_sha256":"88c65ca36f6eb136c05a75abe16cd59ae14250e1c84f315ebb0954a39b59e7fc"}}