{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:22PB6NGKUVOD4BYSRWQPS522EW","short_pith_number":"pith:22PB6NGK","schema_version":"1.0","canonical_sha256":"d69e1f34caa55c3e07128da0f9775a25939c38e5f3438e1273b56025801743c4","source":{"kind":"arxiv","id":"2307.01128","version":1},"attestation_state":"computed","paper":{"title":"Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alessandro Giuliani, Alessandro Sebastian Podda, Leonardo Piano, Livio Pompianu, Salvatore Carta, Sandro Gabriele Tiddia","submitted_at":"2023-07-03T16:01:45Z","abstract_excerpt":"In the current digitalization era, capturing and effectively representing knowledge is crucial in most real-world scenarios. In this context, knowledge graphs represent a potent tool for retrieving and organizing a vast amount of information in a properly interconnected and interpretable structure. However, their generation is still challenging and often requires considerable human effort and domain expertise, hampering the scalability and flexibility across different application fields. This paper proposes an innovative knowledge graph generation approach that leverages the potential of the l"},"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":"2307.01128","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-07-03T16:01:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"6c6d169ad74e6872d2a71d1462e40437a3cd74cd1e89cad35a98cf9a47ac015d","abstract_canon_sha256":"8c808b58a78d8a8597b3050e8489f5833e6b89e53d20517a84bf551739032897"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:27:04.225852Z","signature_b64":"c3uokwyGWwU/o8ZsfDVFqnVAlYaUx0vbnkcoy/p+6BlONLir1xAQLJEpqXA+ASO/zmHwcf6Upe082x6F2Rr7BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d69e1f34caa55c3e07128da0f9775a25939c38e5f3438e1273b56025801743c4","last_reissued_at":"2026-07-05T06:27:04.225424Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:27:04.225424Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Iterative Zero-Shot LLM Prompting for Knowledge Graph Construction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Alessandro Giuliani, Alessandro Sebastian Podda, Leonardo Piano, Livio Pompianu, Salvatore Carta, Sandro Gabriele Tiddia","submitted_at":"2023-07-03T16:01:45Z","abstract_excerpt":"In the current digitalization era, capturing and effectively representing knowledge is crucial in most real-world scenarios. In this context, knowledge graphs represent a potent tool for retrieving and organizing a vast amount of information in a properly interconnected and interpretable structure. However, their generation is still challenging and often requires considerable human effort and domain expertise, hampering the scalability and flexibility across different application fields. This paper proposes an innovative knowledge graph generation approach that leverages the potential of the l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.01128","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/2307.01128/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":"2307.01128","created_at":"2026-07-05T06:27:04.225484+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.01128v1","created_at":"2026-07-05T06:27:04.225484+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.01128","created_at":"2026-07-05T06:27:04.225484+00:00"},{"alias_kind":"pith_short_12","alias_value":"22PB6NGKUVOD","created_at":"2026-07-05T06:27:04.225484+00:00"},{"alias_kind":"pith_short_16","alias_value":"22PB6NGKUVOD4BYS","created_at":"2026-07-05T06:27:04.225484+00:00"},{"alias_kind":"pith_short_8","alias_value":"22PB6NGK","created_at":"2026-07-05T06:27:04.225484+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.19710","citing_title":"FineREX: Fine-Tuned NER-RE for Human Smuggling Knowledge Graphs","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2504.07738","citing_title":"Automated Construction of a Knowledge Graph of Nuclear Fusion Energy for Effective Elicitation and Retrieval of Information","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2509.09544","citing_title":"MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2604.19137","citing_title":"Construction of Knowledge Graph based on Language Model","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15951","citing_title":"Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval","ref_index":33,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/22PB6NGKUVOD4BYSRWQPS522EW","json":"https://pith.science/pith/22PB6NGKUVOD4BYSRWQPS522EW.json","graph_json":"https://pith.science/api/pith-number/22PB6NGKUVOD4BYSRWQPS522EW/graph.json","events_json":"https://pith.science/api/pith-number/22PB6NGKUVOD4BYSRWQPS522EW/events.json","paper":"https://pith.science/paper/22PB6NGK"},"agent_actions":{"view_html":"https://pith.science/pith/22PB6NGKUVOD4BYSRWQPS522EW","download_json":"https://pith.science/pith/22PB6NGKUVOD4BYSRWQPS522EW.json","view_paper":"https://pith.science/paper/22PB6NGK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.01128&json=true","fetch_graph":"https://pith.science/api/pith-number/22PB6NGKUVOD4BYSRWQPS522EW/graph.json","fetch_events":"https://pith.science/api/pith-number/22PB6NGKUVOD4BYSRWQPS522EW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/22PB6NGKUVOD4BYSRWQPS522EW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/22PB6NGKUVOD4BYSRWQPS522EW/action/storage_attestation","attest_author":"https://pith.science/pith/22PB6NGKUVOD4BYSRWQPS522EW/action/author_attestation","sign_citation":"https://pith.science/pith/22PB6NGKUVOD4BYSRWQPS522EW/action/citation_signature","submit_replication":"https://pith.science/pith/22PB6NGKUVOD4BYSRWQPS522EW/action/replication_record"}},"created_at":"2026-07-05T06:27:04.225484+00:00","updated_at":"2026-07-05T06:27:04.225484+00:00"}