{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MHPLDJ4GONP5C33PGJIA66MNGI","short_pith_number":"pith:MHPLDJ4G","canonical_record":{"source":{"id":"2312.02296","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-04T19:26:13Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3af6ec6084e73071ad0f4c24c04de0cc743dda1a3ed3d817f41b1a48b1e23c7f","abstract_canon_sha256":"c310326ebd50f201bb7df30547684b82da6d529caf646ee32111469283fd59e6"},"schema_version":"1.0"},"canonical_sha256":"61deb1a786735fd16f6f32500f798d323f07738e346224772bba3d506f61274c","source":{"kind":"arxiv","id":"2312.02296","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.02296","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"arxiv_version","alias_value":"2312.02296v1","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02296","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"pith_short_12","alias_value":"MHPLDJ4GONP5","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"pith_short_16","alias_value":"MHPLDJ4GONP5C33P","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"pith_short_8","alias_value":"MHPLDJ4G","created_at":"2026-07-05T07:20:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MHPLDJ4GONP5C33PGJIA66MNGI","target":"record","payload":{"canonical_record":{"source":{"id":"2312.02296","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-04T19:26:13Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"3af6ec6084e73071ad0f4c24c04de0cc743dda1a3ed3d817f41b1a48b1e23c7f","abstract_canon_sha256":"c310326ebd50f201bb7df30547684b82da6d529caf646ee32111469283fd59e6"},"schema_version":"1.0"},"canonical_sha256":"61deb1a786735fd16f6f32500f798d323f07738e346224772bba3d506f61274c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:20:15.591899Z","signature_b64":"X9pSw2GeRFqph+VFsG3DxVyiBYnTKI4dT9Uxhh3s2jPSvWB5h9sgluMvryGASV/kBAksIBQO7bfV4C0fvEQqBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61deb1a786735fd16f6f32500f798d323f07738e346224772bba3d506f61274c","last_reissued_at":"2026-07-05T07:20:15.591321Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:20:15.591321Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2312.02296","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:20:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"edpm2m0utY/Wwy4HauflbRxDKQlphSvwDXpINirLFsUXhHOvX8OALHYZ5VMatw1/zQklNN5VeBdejaFH8l80BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T16:03:16.348614Z"},"content_sha256":"192085e69b241c71fbe8431d6409d9a7ce7c7be40444e4a0f1430c6549f49d2d","schema_version":"1.0","event_id":"sha256:192085e69b241c71fbe8431d6409d9a7ce7c7be40444e4a0f1430c6549f49d2d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MHPLDJ4GONP5C33PGJIA66MNGI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LLMs Accelerate Annotation for Medical Information Extraction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Akshay Goel, Almog Gueta, Amir Feder, Bolous Jaber, Chang Liu, Itay Laish, Jean Steiner, Lan Huong Nguyen, Omry Gilon, Rupesh Kartha, Shashir Reddy, Sofia Erell, Xiaohong Hao","submitted_at":"2023-12-04T19:26:13Z","abstract_excerpt":"The unstructured nature of clinical notes within electronic health records often conceals vital patient-related information, making it challenging to access or interpret. To uncover this hidden information, specialized Natural Language Processing (NLP) models are required. However, training these models necessitates large amounts of labeled data, a process that is both time-consuming and costly when relying solely on human experts for annotation. In this paper, we propose an approach that combines Large Language Models (LLMs) with human expertise to create an efficient method for generating gr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02296","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/2312.02296/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":"2026-07-05T07:20:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hU6X6P7U9QVXtHPUuJ+uC972JPT4OHGMn5GD0pIeXdiGLqAcMJcUvcVQdC2I9IBBYluXjSnX8NQxMn7qIDsRAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T16:03:16.349148Z"},"content_sha256":"279d431824b8fe0abac4b3de59aafaa6e0da085b518cc6a1e1d182bf377e9ee9","schema_version":"1.0","event_id":"sha256:279d431824b8fe0abac4b3de59aafaa6e0da085b518cc6a1e1d182bf377e9ee9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MHPLDJ4GONP5C33PGJIA66MNGI/bundle.json","state_url":"https://pith.science/pith/MHPLDJ4GONP5C33PGJIA66MNGI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MHPLDJ4GONP5C33PGJIA66MNGI/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":"2026-08-21T16:03:16Z","links":{"resolver":"https://pith.science/pith/MHPLDJ4GONP5C33PGJIA66MNGI","bundle":"https://pith.science/pith/MHPLDJ4GONP5C33PGJIA66MNGI/bundle.json","state":"https://pith.science/pith/MHPLDJ4GONP5C33PGJIA66MNGI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MHPLDJ4GONP5C33PGJIA66MNGI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MHPLDJ4GONP5C33PGJIA66MNGI","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":"c310326ebd50f201bb7df30547684b82da6d529caf646ee32111469283fd59e6","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-04T19:26:13Z","title_canon_sha256":"3af6ec6084e73071ad0f4c24c04de0cc743dda1a3ed3d817f41b1a48b1e23c7f"},"schema_version":"1.0","source":{"id":"2312.02296","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2312.02296","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"arxiv_version","alias_value":"2312.02296v1","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02296","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"pith_short_12","alias_value":"MHPLDJ4GONP5","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"pith_short_16","alias_value":"MHPLDJ4GONP5C33P","created_at":"2026-07-05T07:20:15Z"},{"alias_kind":"pith_short_8","alias_value":"MHPLDJ4G","created_at":"2026-07-05T07:20:15Z"}],"graph_snapshots":[{"event_id":"sha256:279d431824b8fe0abac4b3de59aafaa6e0da085b518cc6a1e1d182bf377e9ee9","target":"graph","created_at":"2026-07-05T07:20:15Z","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/2312.02296/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The unstructured nature of clinical notes within electronic health records often conceals vital patient-related information, making it challenging to access or interpret. To uncover this hidden information, specialized Natural Language Processing (NLP) models are required. However, training these models necessitates large amounts of labeled data, a process that is both time-consuming and costly when relying solely on human experts for annotation. In this paper, we propose an approach that combines Large Language Models (LLMs) with human expertise to create an efficient method for generating gr","authors_text":"Akshay Goel, Almog Gueta, Amir Feder, Bolous Jaber, Chang Liu, Itay Laish, Jean Steiner, Lan Huong Nguyen, Omry Gilon, Rupesh Kartha, Shashir Reddy, Sofia Erell, Xiaohong Hao","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-04T19:26:13Z","title":"LLMs Accelerate Annotation for Medical Information Extraction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02296","kind":"arxiv","version":1},"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":"sha256:192085e69b241c71fbe8431d6409d9a7ce7c7be40444e4a0f1430c6549f49d2d","target":"record","created_at":"2026-07-05T07:20:15Z","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":"c310326ebd50f201bb7df30547684b82da6d529caf646ee32111469283fd59e6","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-12-04T19:26:13Z","title_canon_sha256":"3af6ec6084e73071ad0f4c24c04de0cc743dda1a3ed3d817f41b1a48b1e23c7f"},"schema_version":"1.0","source":{"id":"2312.02296","kind":"arxiv","version":1}},"canonical_sha256":"61deb1a786735fd16f6f32500f798d323f07738e346224772bba3d506f61274c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"61deb1a786735fd16f6f32500f798d323f07738e346224772bba3d506f61274c","first_computed_at":"2026-07-05T07:20:15.591321Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:20:15.591321Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"X9pSw2GeRFqph+VFsG3DxVyiBYnTKI4dT9Uxhh3s2jPSvWB5h9sgluMvryGASV/kBAksIBQO7bfV4C0fvEQqBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:20:15.591899Z","signed_message":"canonical_sha256_bytes"},"source_id":"2312.02296","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:192085e69b241c71fbe8431d6409d9a7ce7c7be40444e4a0f1430c6549f49d2d","sha256:279d431824b8fe0abac4b3de59aafaa6e0da085b518cc6a1e1d182bf377e9ee9"],"state_sha256":"ceab8bf61b9cfaba83274b315649e0c46578e89c328ff24d130352d43c9cb439"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sJKplapNJj1BpTrtcWwIKuKgyXMc8oAHFMFewie1QvgQlcNTm2GgHpOTsB5MrsAXcIihZe7Yev9EXlvZtYJjDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T16:03:16.353943Z","bundle_sha256":"d8e155eaa1fdd7df8b4b005fe6f1cd2143e9809afd5389d606b2a23f018fd9f7"}}