{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:5C2QBVD7MGCQA2EZD7VZBIURZI","short_pith_number":"pith:5C2QBVD7","schema_version":"1.0","canonical_sha256":"e8b500d47f61850068991feb90a291ca05a114dea058a2c3778f1a3922381cd0","source":{"kind":"arxiv","id":"1710.08691","version":3},"attestation_state":"computed","paper":{"title":"BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Axel-Cyrille Ngonga Ngomo, Diego Moussallem, Michael R\\\"oder, Ren\\'e Speck, Ricardo Usbeck","submitted_at":"2017-10-24T10:15:58Z","abstract_excerpt":"The manual creation of gold standards for named entity recognition and entity linking is time- and resource-intensive. Moreover, recent works show that such gold standards contain a large proportion of mistakes in addition to being difficult to maintain. We hence present BENGAL, a novel automatic generation of such gold standards as a complement to manually created benchmarks. The main advantage of our benchmarks is that they can be readily generated at any time. They are also cost-effective while being guaranteed to be free of annotation errors. We compare the performance of 11 tools on bench"},"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":"1710.08691","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-10-24T10:15:58Z","cross_cats_sorted":[],"title_canon_sha256":"f37d66ce866d909c1f8a9aa8f5201d357119f565a6b1dee6f3628f60696b9ead","abstract_canon_sha256":"a07337a8381dd7c81340db14cbaa535e95dd30f855d812fdec0bbf883eaf0ae7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:47.409128Z","signature_b64":"kgGLM1ix2X+8xvIWki0nO/dLAFZeYE4zVmy0UnwzO77IKmjffaNIdQtyk8lZ6ZHBXzB14HW/dCT3TZRuPXmGDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8b500d47f61850068991feb90a291ca05a114dea058a2c3778f1a3922381cd0","last_reissued_at":"2026-05-18T00:01:47.408420Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:47.408420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BENGAL: An Automatic Benchmark Generator for Entity Recognition and Linking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Axel-Cyrille Ngonga Ngomo, Diego Moussallem, Michael R\\\"oder, Ren\\'e Speck, Ricardo Usbeck","submitted_at":"2017-10-24T10:15:58Z","abstract_excerpt":"The manual creation of gold standards for named entity recognition and entity linking is time- and resource-intensive. Moreover, recent works show that such gold standards contain a large proportion of mistakes in addition to being difficult to maintain. We hence present BENGAL, a novel automatic generation of such gold standards as a complement to manually created benchmarks. The main advantage of our benchmarks is that they can be readily generated at any time. They are also cost-effective while being guaranteed to be free of annotation errors. We compare the performance of 11 tools on bench"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.08691","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1710.08691","created_at":"2026-05-18T00:01:47.408533+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.08691v3","created_at":"2026-05-18T00:01:47.408533+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.08691","created_at":"2026-05-18T00:01:47.408533+00:00"},{"alias_kind":"pith_short_12","alias_value":"5C2QBVD7MGCQ","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"5C2QBVD7MGCQA2EZ","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"5C2QBVD7","created_at":"2026-05-18T12:31:00.734936+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/5C2QBVD7MGCQA2EZD7VZBIURZI","json":"https://pith.science/pith/5C2QBVD7MGCQA2EZD7VZBIURZI.json","graph_json":"https://pith.science/api/pith-number/5C2QBVD7MGCQA2EZD7VZBIURZI/graph.json","events_json":"https://pith.science/api/pith-number/5C2QBVD7MGCQA2EZD7VZBIURZI/events.json","paper":"https://pith.science/paper/5C2QBVD7"},"agent_actions":{"view_html":"https://pith.science/pith/5C2QBVD7MGCQA2EZD7VZBIURZI","download_json":"https://pith.science/pith/5C2QBVD7MGCQA2EZD7VZBIURZI.json","view_paper":"https://pith.science/paper/5C2QBVD7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.08691&json=true","fetch_graph":"https://pith.science/api/pith-number/5C2QBVD7MGCQA2EZD7VZBIURZI/graph.json","fetch_events":"https://pith.science/api/pith-number/5C2QBVD7MGCQA2EZD7VZBIURZI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5C2QBVD7MGCQA2EZD7VZBIURZI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5C2QBVD7MGCQA2EZD7VZBIURZI/action/storage_attestation","attest_author":"https://pith.science/pith/5C2QBVD7MGCQA2EZD7VZBIURZI/action/author_attestation","sign_citation":"https://pith.science/pith/5C2QBVD7MGCQA2EZD7VZBIURZI/action/citation_signature","submit_replication":"https://pith.science/pith/5C2QBVD7MGCQA2EZD7VZBIURZI/action/replication_record"}},"created_at":"2026-05-18T00:01:47.408533+00:00","updated_at":"2026-05-18T00:01:47.408533+00:00"}