{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:M74KOUW3LMFSAXM6DTJMS4HK2E","short_pith_number":"pith:M74KOUW3","schema_version":"1.0","canonical_sha256":"67f8a752db5b0b205d9e1cd2c970ead11bbfd8300472f1ef0711324d9f92d15e","source":{"kind":"arxiv","id":"2310.10590","version":1},"attestation_state":"computed","paper":{"title":"Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bin Xu, Jifan Yu, Ji Qi, Juanzi Li, Kaisheng Zeng, Kaixuan Ji, Lei Hou, Xiaozhi Wang","submitted_at":"2023-10-16T17:11:42Z","abstract_excerpt":"Open Information Extraction (OIE) aims to extract objective structured knowledge from natural texts, which has attracted growing attention to build dedicated models with human experience. As the large language models (LLMs) have exhibited remarkable in-context learning capabilities, a question arises as to whether the task of OIE can be effectively tackled with this paradigm? In this paper, we explore solving the OIE problem by constructing an appropriate reasoning environment for LLMs. Specifically, we first propose a method to effectively estimate the discrepancy of syntactic distribution be"},"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":"2310.10590","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-16T17:11:42Z","cross_cats_sorted":[],"title_canon_sha256":"ec2233d809b80ccea078ee139a4d9b6ffec71ea9f4432e4d903a3fa0259354c6","abstract_canon_sha256":"88b3a89e8eb9dc4634833810d39f30a3a2e63608b4dbfbae2e859b3dd61f186d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:21.957243Z","signature_b64":"YC2Ak/RZOVfZa1HeiMOfvxl6dzbx9hEFtaE9qAhrEGCdSXdZv5+rilepJMrbPOLlLopxgsZBu2S9vCQeuYlaDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67f8a752db5b0b205d9e1cd2c970ead11bbfd8300472f1ef0711324d9f92d15e","last_reissued_at":"2026-07-05T07:01:21.956753Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:21.956753Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mastering the Task of Open Information Extraction with Large Language Models and Consistent Reasoning Environment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bin Xu, Jifan Yu, Ji Qi, Juanzi Li, Kaisheng Zeng, Kaixuan Ji, Lei Hou, Xiaozhi Wang","submitted_at":"2023-10-16T17:11:42Z","abstract_excerpt":"Open Information Extraction (OIE) aims to extract objective structured knowledge from natural texts, which has attracted growing attention to build dedicated models with human experience. As the large language models (LLMs) have exhibited remarkable in-context learning capabilities, a question arises as to whether the task of OIE can be effectively tackled with this paradigm? In this paper, we explore solving the OIE problem by constructing an appropriate reasoning environment for LLMs. Specifically, we first propose a method to effectively estimate the discrepancy of syntactic distribution be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.10590","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/2310.10590/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":"2310.10590","created_at":"2026-07-05T07:01:21.956820+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.10590v1","created_at":"2026-07-05T07:01:21.956820+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.10590","created_at":"2026-07-05T07:01:21.956820+00:00"},{"alias_kind":"pith_short_12","alias_value":"M74KOUW3LMFS","created_at":"2026-07-05T07:01:21.956820+00:00"},{"alias_kind":"pith_short_16","alias_value":"M74KOUW3LMFSAXM6","created_at":"2026-07-05T07:01:21.956820+00:00"},{"alias_kind":"pith_short_8","alias_value":"M74KOUW3","created_at":"2026-07-05T07:01:21.956820+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/M74KOUW3LMFSAXM6DTJMS4HK2E","json":"https://pith.science/pith/M74KOUW3LMFSAXM6DTJMS4HK2E.json","graph_json":"https://pith.science/api/pith-number/M74KOUW3LMFSAXM6DTJMS4HK2E/graph.json","events_json":"https://pith.science/api/pith-number/M74KOUW3LMFSAXM6DTJMS4HK2E/events.json","paper":"https://pith.science/paper/M74KOUW3"},"agent_actions":{"view_html":"https://pith.science/pith/M74KOUW3LMFSAXM6DTJMS4HK2E","download_json":"https://pith.science/pith/M74KOUW3LMFSAXM6DTJMS4HK2E.json","view_paper":"https://pith.science/paper/M74KOUW3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.10590&json=true","fetch_graph":"https://pith.science/api/pith-number/M74KOUW3LMFSAXM6DTJMS4HK2E/graph.json","fetch_events":"https://pith.science/api/pith-number/M74KOUW3LMFSAXM6DTJMS4HK2E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M74KOUW3LMFSAXM6DTJMS4HK2E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M74KOUW3LMFSAXM6DTJMS4HK2E/action/storage_attestation","attest_author":"https://pith.science/pith/M74KOUW3LMFSAXM6DTJMS4HK2E/action/author_attestation","sign_citation":"https://pith.science/pith/M74KOUW3LMFSAXM6DTJMS4HK2E/action/citation_signature","submit_replication":"https://pith.science/pith/M74KOUW3LMFSAXM6DTJMS4HK2E/action/replication_record"}},"created_at":"2026-07-05T07:01:21.956820+00:00","updated_at":"2026-07-05T07:01:21.956820+00:00"}