{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:D7VC4IVZZDYFSOLTVJD2EYWM7O","short_pith_number":"pith:D7VC4IVZ","schema_version":"1.0","canonical_sha256":"1fea2e22b9c8f0593973aa47a262ccfb9eb161ada537db2b5bbce7a186ed33a8","source":{"kind":"arxiv","id":"2303.03836","version":2},"attestation_state":"computed","paper":{"title":"Exploring the Feasibility of ChatGPT for Event Extraction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changlong Yu, Huan Zhao, Jun Gao, Ruifeng Xu","submitted_at":"2023-03-07T12:03:58Z","abstract_excerpt":"Event extraction is a fundamental task in natural language processing that involves identifying and extracting information about events mentioned in text. However, it is a challenging task due to the lack of annotated data, which is expensive and time-consuming to obtain. The emergence of large language models (LLMs) such as ChatGPT provides an opportunity to solve language tasks with simple prompts without the need for task-specific datasets and fine-tuning. While ChatGPT has demonstrated impressive results in tasks like machine translation, text summarization, and question answering, it pres"},"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":"2303.03836","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-03-07T12:03:58Z","cross_cats_sorted":[],"title_canon_sha256":"2c70eea41480dc6d07a3cec8530900802b113a085aadef12a8a9a5951d05f2c5","abstract_canon_sha256":"923e6c1d133c2ec0422e44d91aa74aef54b4bed82a4470132186ec8ab14b315e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:33.847988Z","signature_b64":"5y+RAddcANX4DfuB0ScMz/kFCxDlmr0JoSFIFuWMQdUEh7SpEolrjTfCxYrQc43XgMwOkkrRDbk+dh1FACAxAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1fea2e22b9c8f0593973aa47a262ccfb9eb161ada537db2b5bbce7a186ed33a8","last_reissued_at":"2026-07-05T05:49:33.847635Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:33.847635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring the Feasibility of ChatGPT for Event Extraction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Changlong Yu, Huan Zhao, Jun Gao, Ruifeng Xu","submitted_at":"2023-03-07T12:03:58Z","abstract_excerpt":"Event extraction is a fundamental task in natural language processing that involves identifying and extracting information about events mentioned in text. However, it is a challenging task due to the lack of annotated data, which is expensive and time-consuming to obtain. The emergence of large language models (LLMs) such as ChatGPT provides an opportunity to solve language tasks with simple prompts without the need for task-specific datasets and fine-tuning. While ChatGPT has demonstrated impressive results in tasks like machine translation, text summarization, and question answering, it pres"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.03836","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/2303.03836/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":"2303.03836","created_at":"2026-07-05T05:49:33.847690+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.03836v2","created_at":"2026-07-05T05:49:33.847690+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.03836","created_at":"2026-07-05T05:49:33.847690+00:00"},{"alias_kind":"pith_short_12","alias_value":"D7VC4IVZZDYF","created_at":"2026-07-05T05:49:33.847690+00:00"},{"alias_kind":"pith_short_16","alias_value":"D7VC4IVZZDYFSOLT","created_at":"2026-07-05T05:49:33.847690+00:00"},{"alias_kind":"pith_short_8","alias_value":"D7VC4IVZ","created_at":"2026-07-05T05:49:33.847690+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2406.14075","citing_title":"EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain","ref_index":6,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D7VC4IVZZDYFSOLTVJD2EYWM7O","json":"https://pith.science/pith/D7VC4IVZZDYFSOLTVJD2EYWM7O.json","graph_json":"https://pith.science/api/pith-number/D7VC4IVZZDYFSOLTVJD2EYWM7O/graph.json","events_json":"https://pith.science/api/pith-number/D7VC4IVZZDYFSOLTVJD2EYWM7O/events.json","paper":"https://pith.science/paper/D7VC4IVZ"},"agent_actions":{"view_html":"https://pith.science/pith/D7VC4IVZZDYFSOLTVJD2EYWM7O","download_json":"https://pith.science/pith/D7VC4IVZZDYFSOLTVJD2EYWM7O.json","view_paper":"https://pith.science/paper/D7VC4IVZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.03836&json=true","fetch_graph":"https://pith.science/api/pith-number/D7VC4IVZZDYFSOLTVJD2EYWM7O/graph.json","fetch_events":"https://pith.science/api/pith-number/D7VC4IVZZDYFSOLTVJD2EYWM7O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D7VC4IVZZDYFSOLTVJD2EYWM7O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D7VC4IVZZDYFSOLTVJD2EYWM7O/action/storage_attestation","attest_author":"https://pith.science/pith/D7VC4IVZZDYFSOLTVJD2EYWM7O/action/author_attestation","sign_citation":"https://pith.science/pith/D7VC4IVZZDYFSOLTVJD2EYWM7O/action/citation_signature","submit_replication":"https://pith.science/pith/D7VC4IVZZDYFSOLTVJD2EYWM7O/action/replication_record"}},"created_at":"2026-07-05T05:49:33.847690+00:00","updated_at":"2026-07-05T05:49:33.847690+00:00"}