{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:JWT4RCDJUVXX3OYDWV2OSIYV7A","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1b13880f27cd1f0d0c42b296cfdb089571577e81b4b22b9a24f08315b46ceb76","cross_cats_sorted":["cs.DL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-08T12:18:11Z","title_canon_sha256":"7961769a9612f706f627cd87edfca0ed78b03afa16503e53a2ab4f928e9cc28a"},"schema_version":"1.0","source":{"id":"2501.04455","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.04455","created_at":"2026-07-05T09:58:35Z"},{"alias_kind":"arxiv_version","alias_value":"2501.04455v1","created_at":"2026-07-05T09:58:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.04455","created_at":"2026-07-05T09:58:35Z"},{"alias_kind":"pith_short_12","alias_value":"JWT4RCDJUVXX","created_at":"2026-07-05T09:58:35Z"},{"alias_kind":"pith_short_16","alias_value":"JWT4RCDJUVXX3OYD","created_at":"2026-07-05T09:58:35Z"},{"alias_kind":"pith_short_8","alias_value":"JWT4RCDJ","created_at":"2026-07-05T09:58:35Z"}],"graph_snapshots":[{"event_id":"sha256:594d613ea33b62b611679a8e41e152681a6bba54f1055f941aa3b3c05a416e1a","target":"graph","created_at":"2026-07-05T09:58:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.04455/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Named entity recognition is an important task when constructing knowledge bases from unstructured data sources. Whereas entity detection methods mostly rely on extensive training data, Large Language Models (LLMs) have paved the way towards approaches that rely on zero-shot learning (ZSL) or few-shot learning (FSL) by taking advantage of the capabilities LLMs acquired during pretraining. Specifically, in very specialized scenarios where large-scale training data is not available, ZSL / FSL opens new opportunities. This paper follows this recent trend and investigates the potential of leveragin","authors_text":"Brigitte Mathiak, Danilo Dessi, Lu Gan, Martin Blum, Ralf Schenkel, Stefan Dietze","cross_cats":["cs.DL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-08T12:18:11Z","title":"Hidden Entity Detection from GitHub Leveraging Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.04455","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3a75704c24361701524e79f463592f7520eddeeb2a412f19a9f307d571412eb0","target":"record","created_at":"2026-07-05T09:58:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1b13880f27cd1f0d0c42b296cfdb089571577e81b4b22b9a24f08315b46ceb76","cross_cats_sorted":["cs.DL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-08T12:18:11Z","title_canon_sha256":"7961769a9612f706f627cd87edfca0ed78b03afa16503e53a2ab4f928e9cc28a"},"schema_version":"1.0","source":{"id":"2501.04455","kind":"arxiv","version":1}},"canonical_sha256":"4da7c88869a56f7dbb03b574e92315f82a621e6bc344011704d88aebbdf48049","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4da7c88869a56f7dbb03b574e92315f82a621e6bc344011704d88aebbdf48049","first_computed_at":"2026-07-05T09:58:35.457056Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:58:35.457056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YlTXOWj9/ektrG8mdMKHM5x3m3qbuBJstNWH5Bq3JxbXeQ4RSCBMol4ZJjw5/pN6i664mC0HlwGDEA7l0XEEBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:58:35.457653Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.04455","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3a75704c24361701524e79f463592f7520eddeeb2a412f19a9f307d571412eb0","sha256:594d613ea33b62b611679a8e41e152681a6bba54f1055f941aa3b3c05a416e1a"],"state_sha256":"08ec2011b00cfea56ed4135dc34ded25b64fe63c92531498f66669a2e0c3ef24"}