{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BXZTMODDBXGJGACCJYOU5CR2RJ","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":"6ca25b702093517c7688fe22eca0d38dde036448cfb577b92b25ddcd6543c794","cross_cats_sorted":["cs.AI","cs.DB"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-05T15:40:24Z","title_canon_sha256":"bb6ca4b418eab066d30a422962953ae493b04266118599c74de92ddade1564b6"},"schema_version":"1.0","source":{"id":"2505.02737","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.02737","created_at":"2026-07-05T10:59:10Z"},{"alias_kind":"arxiv_version","alias_value":"2505.02737v2","created_at":"2026-07-05T10:59:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.02737","created_at":"2026-07-05T10:59:10Z"},{"alias_kind":"pith_short_12","alias_value":"BXZTMODDBXGJ","created_at":"2026-07-05T10:59:10Z"},{"alias_kind":"pith_short_16","alias_value":"BXZTMODDBXGJGACC","created_at":"2026-07-05T10:59:10Z"},{"alias_kind":"pith_short_8","alias_value":"BXZTMODD","created_at":"2026-07-05T10:59:10Z"}],"graph_snapshots":[{"event_id":"sha256:ba6c137123e95388e17a215605a227abb93ded4a9d80ea90e29bf60a8592700a","target":"graph","created_at":"2026-07-05T10:59:10Z","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/2505.02737/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in Large Language Models (LLMs) have positioned them as a prominent solution for Natural Language Processing tasks. Notably, they can approach these problems in a zero or few-shot manner, thereby eliminating the need for training or fine-tuning task-specific models. However, LLMs face some challenges, including hallucination and the presence of outdated knowledge or missing information from specific domains in the training data. These problems cannot be easily solved by retraining the models with new data as it is a time-consuming and expensive process. To mitigate these issues","authors_text":"Anna Queralt, Besim Bilalli, Gerard Pons","cross_cats":["cs.AI","cs.DB"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-05T15:40:24Z","title":"Knowledge Graphs for Enhancing Large Language Models in Entity Disambiguation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.02737","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:fdea3090683a1d0576f44ad61676dd4dee71975717cf186a0f29446d35ab7a41","target":"record","created_at":"2026-07-05T10:59:10Z","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":"6ca25b702093517c7688fe22eca0d38dde036448cfb577b92b25ddcd6543c794","cross_cats_sorted":["cs.AI","cs.DB"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-05T15:40:24Z","title_canon_sha256":"bb6ca4b418eab066d30a422962953ae493b04266118599c74de92ddade1564b6"},"schema_version":"1.0","source":{"id":"2505.02737","kind":"arxiv","version":2}},"canonical_sha256":"0df33638630dcc9300424e1d4e8a3a8a7f3f81e6d9b82b64105d2d7718a982f8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0df33638630dcc9300424e1d4e8a3a8a7f3f81e6d9b82b64105d2d7718a982f8","first_computed_at":"2026-07-05T10:59:10.576259Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:59:10.576259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"N+FWJnKtDDoqRVCkJjHARALAP6I+A2imvKNCk/MeXKIdOhQ1QPBoJCc8R2sQrOuxP1lDHQ/uw+NbyIM1WuJXBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:59:10.576739Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.02737","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fdea3090683a1d0576f44ad61676dd4dee71975717cf186a0f29446d35ab7a41","sha256:ba6c137123e95388e17a215605a227abb93ded4a9d80ea90e29bf60a8592700a"],"state_sha256":"ccc714253d3a64e6b07a584e555f47db36297cf8c06123d77a7082d4b8d7bdbb"}