{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:L35QKAYXROEGGZHLJ4DLTKWJJN","short_pith_number":"pith:L35QKAYX","schema_version":"1.0","canonical_sha256":"5efb0503178b886364eb4f06b9aac94b6a2506b6a20244cee9baeeb1af11ae63","source":{"kind":"arxiv","id":"2406.04528","version":1},"attestation_state":"computed","paper":{"title":"llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Claudio Aracena, Fabi\\'an Villena, Luis Miranda","submitted_at":"2024-06-06T22:01:59Z","abstract_excerpt":"Large language models (LLMs) allow us to generate high-quality human-like text. One interesting task in natural language processing (NLP) is named entity recognition (NER), which seeks to detect mentions of relevant information in documents. This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with LLMs; by providing an easy-to-use interface, llmNER can compose prompts, query the model, and parse the completion returned by the LLM. Also, the library enables the user to perform prompt engineering efficiently by providing a simple interface to test multiple va"},"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":"2406.04528","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-06-06T22:01:59Z","cross_cats_sorted":[],"title_canon_sha256":"05d5c63ea2f73f5b8cfabe1ea4b0c87aac6538e88660206371f9201a1628ca0b","abstract_canon_sha256":"49b5fe878a0d600fbe1b5f0ebc254708d6a6db1947d7eb453707130f8c544d09"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:28:45.138545Z","signature_b64":"Qsxt4vVYwFAlpiRvN/mF+fO8v2S7Hs0tleRXB3g6By26+p+4Txxlgjs6cYGWC/4ljsejl6PSdjcC0hT9k3NzDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5efb0503178b886364eb4f06b9aac94b6a2506b6a20244cee9baeeb1af11ae63","last_reissued_at":"2026-07-05T08:28:45.138107Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:28:45.138107Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"llmNER: (Zero|Few)-Shot Named Entity Recognition, Exploiting the Power of Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Claudio Aracena, Fabi\\'an Villena, Luis Miranda","submitted_at":"2024-06-06T22:01:59Z","abstract_excerpt":"Large language models (LLMs) allow us to generate high-quality human-like text. One interesting task in natural language processing (NLP) is named entity recognition (NER), which seeks to detect mentions of relevant information in documents. This paper presents llmNER, a Python library for implementing zero-shot and few-shot NER with LLMs; by providing an easy-to-use interface, llmNER can compose prompts, query the model, and parse the completion returned by the LLM. Also, the library enables the user to perform prompt engineering efficiently by providing a simple interface to test multiple va"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.04528","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/2406.04528/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":"2406.04528","created_at":"2026-07-05T08:28:45.138163+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.04528v1","created_at":"2026-07-05T08:28:45.138163+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.04528","created_at":"2026-07-05T08:28:45.138163+00:00"},{"alias_kind":"pith_short_12","alias_value":"L35QKAYXROEG","created_at":"2026-07-05T08:28:45.138163+00:00"},{"alias_kind":"pith_short_16","alias_value":"L35QKAYXROEGGZHL","created_at":"2026-07-05T08:28:45.138163+00:00"},{"alias_kind":"pith_short_8","alias_value":"L35QKAYX","created_at":"2026-07-05T08:28:45.138163+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.07303","citing_title":"KRONE: Scalable LLM-Augmented Log Anomaly Detection via Hierarchical Abstraction","ref_index":52,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L35QKAYXROEGGZHLJ4DLTKWJJN","json":"https://pith.science/pith/L35QKAYXROEGGZHLJ4DLTKWJJN.json","graph_json":"https://pith.science/api/pith-number/L35QKAYXROEGGZHLJ4DLTKWJJN/graph.json","events_json":"https://pith.science/api/pith-number/L35QKAYXROEGGZHLJ4DLTKWJJN/events.json","paper":"https://pith.science/paper/L35QKAYX"},"agent_actions":{"view_html":"https://pith.science/pith/L35QKAYXROEGGZHLJ4DLTKWJJN","download_json":"https://pith.science/pith/L35QKAYXROEGGZHLJ4DLTKWJJN.json","view_paper":"https://pith.science/paper/L35QKAYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.04528&json=true","fetch_graph":"https://pith.science/api/pith-number/L35QKAYXROEGGZHLJ4DLTKWJJN/graph.json","fetch_events":"https://pith.science/api/pith-number/L35QKAYXROEGGZHLJ4DLTKWJJN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L35QKAYXROEGGZHLJ4DLTKWJJN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L35QKAYXROEGGZHLJ4DLTKWJJN/action/storage_attestation","attest_author":"https://pith.science/pith/L35QKAYXROEGGZHLJ4DLTKWJJN/action/author_attestation","sign_citation":"https://pith.science/pith/L35QKAYXROEGGZHLJ4DLTKWJJN/action/citation_signature","submit_replication":"https://pith.science/pith/L35QKAYXROEGGZHLJ4DLTKWJJN/action/replication_record"}},"created_at":"2026-07-05T08:28:45.138163+00:00","updated_at":"2026-07-05T08:28:45.138163+00:00"}