{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:W7Y53Y25HV5LC3JXFN2V6HNN6G","short_pith_number":"pith:W7Y53Y25","schema_version":"1.0","canonical_sha256":"b7f1dde35d3d7ab16d372b755f1dadf19e94ff919aff409e3bed3a0b42523a9b","source":{"kind":"arxiv","id":"2305.14847","version":1},"attestation_state":"computed","paper":{"title":"Drafting Event Schemas using Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anisha Gunjal, Greg Durrett","submitted_at":"2023-05-24T07:57:04Z","abstract_excerpt":"Past work has studied event prediction and event language modeling, sometimes mediated through structured representations of knowledge in the form of event schemas. Such schemas can lead to explainable predictions and forecasting of unseen events given incomplete information. In this work, we look at the process of creating such schemas to describe complex events. We use large language models (LLMs) to draft schemas directly in natural language, which can be further refined by human curators as necessary. Our focus is on whether we can achieve sufficient diversity and recall of key events and "},"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":"2305.14847","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T07:57:04Z","cross_cats_sorted":[],"title_canon_sha256":"04056de7874083b08886fc790a7b6b1d5494655ccd6afc944c5c4c6fbbaa56e9","abstract_canon_sha256":"f52b424b73275895a126b4c69d2cfc0a075f9e0c0c2170628a072c9c86c5c7c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:13:28.495428Z","signature_b64":"JqEyTLy+9yF0vqwORzEdxDrsyvWiT2uv9e0WgVVzqM2X/40WqpPhD029f98bdzsqPYIlosax6i6Hd/K3ckGECQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7f1dde35d3d7ab16d372b755f1dadf19e94ff919aff409e3bed3a0b42523a9b","last_reissued_at":"2026-07-05T06:13:28.495006Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:13:28.495006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Drafting Event Schemas using Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Anisha Gunjal, Greg Durrett","submitted_at":"2023-05-24T07:57:04Z","abstract_excerpt":"Past work has studied event prediction and event language modeling, sometimes mediated through structured representations of knowledge in the form of event schemas. Such schemas can lead to explainable predictions and forecasting of unseen events given incomplete information. In this work, we look at the process of creating such schemas to describe complex events. We use large language models (LLMs) to draft schemas directly in natural language, which can be further refined by human curators as necessary. Our focus is on whether we can achieve sufficient diversity and recall of key events and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.14847","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/2305.14847/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":"2305.14847","created_at":"2026-07-05T06:13:28.495066+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.14847v1","created_at":"2026-07-05T06:13:28.495066+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.14847","created_at":"2026-07-05T06:13:28.495066+00:00"},{"alias_kind":"pith_short_12","alias_value":"W7Y53Y25HV5L","created_at":"2026-07-05T06:13:28.495066+00:00"},{"alias_kind":"pith_short_16","alias_value":"W7Y53Y25HV5LC3JX","created_at":"2026-07-05T06:13:28.495066+00:00"},{"alias_kind":"pith_short_8","alias_value":"W7Y53Y25","created_at":"2026-07-05T06:13:28.495066+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06910","citing_title":"Causal Graph based Event Reasoning using Semantic Relation Experts","ref_index":424,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W7Y53Y25HV5LC3JXFN2V6HNN6G","json":"https://pith.science/pith/W7Y53Y25HV5LC3JXFN2V6HNN6G.json","graph_json":"https://pith.science/api/pith-number/W7Y53Y25HV5LC3JXFN2V6HNN6G/graph.json","events_json":"https://pith.science/api/pith-number/W7Y53Y25HV5LC3JXFN2V6HNN6G/events.json","paper":"https://pith.science/paper/W7Y53Y25"},"agent_actions":{"view_html":"https://pith.science/pith/W7Y53Y25HV5LC3JXFN2V6HNN6G","download_json":"https://pith.science/pith/W7Y53Y25HV5LC3JXFN2V6HNN6G.json","view_paper":"https://pith.science/paper/W7Y53Y25","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.14847&json=true","fetch_graph":"https://pith.science/api/pith-number/W7Y53Y25HV5LC3JXFN2V6HNN6G/graph.json","fetch_events":"https://pith.science/api/pith-number/W7Y53Y25HV5LC3JXFN2V6HNN6G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W7Y53Y25HV5LC3JXFN2V6HNN6G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W7Y53Y25HV5LC3JXFN2V6HNN6G/action/storage_attestation","attest_author":"https://pith.science/pith/W7Y53Y25HV5LC3JXFN2V6HNN6G/action/author_attestation","sign_citation":"https://pith.science/pith/W7Y53Y25HV5LC3JXFN2V6HNN6G/action/citation_signature","submit_replication":"https://pith.science/pith/W7Y53Y25HV5LC3JXFN2V6HNN6G/action/replication_record"}},"created_at":"2026-07-05T06:13:28.495066+00:00","updated_at":"2026-07-05T06:13:28.495066+00:00"}