{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7WJIT32GWQ62ZHQORUYEXLEWKF","short_pith_number":"pith:7WJIT32G","schema_version":"1.0","canonical_sha256":"fd9289ef46b43dac9e0e8d304bac96515b7766c881c9988628d797174272e52f","source":{"kind":"arxiv","id":"2210.01613","version":1},"attestation_state":"computed","paper":{"title":"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alham Fikri Aji, Amir Saffari, Priyanka Sen","submitted_at":"2022-10-04T13:54:29Z","abstract_excerpt":"We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers. We run baselines over Mintaka, the best of which achieves 38% hits@1 i"},"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":"2210.01613","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-10-04T13:54:29Z","cross_cats_sorted":[],"title_canon_sha256":"04bc9cf21428394e1e7e5586a292001e3ad39058d7289a4a9655c101fba57dce","abstract_canon_sha256":"ed16934bae06afc085ae7444fd3d1843b5c19aa33ea97f2ce904095fe3c23509"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:03:12.527220Z","signature_b64":"G0PJQXseow+nNjus2UPF77/gbNzg2qmxtwMNMBfekM6nSoTX4OohuwSVlgzrhuoqRD6kfhTY6k8OCCQH8RcXAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd9289ef46b43dac9e0e8d304bac96515b7766c881c9988628d797174272e52f","last_reissued_at":"2026-07-05T05:03:12.526772Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:03:12.526772Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alham Fikri Aji, Amir Saffari, Priyanka Sen","submitted_at":"2022-10-04T13:54:29Z","abstract_excerpt":"We introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish for a total of 180,000 samples. Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, which were naturally elicited from crowd workers. We run baselines over Mintaka, the best of which achieves 38% hits@1 i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.01613","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/2210.01613/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":"2210.01613","created_at":"2026-07-05T05:03:12.526830+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.01613v1","created_at":"2026-07-05T05:03:12.526830+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.01613","created_at":"2026-07-05T05:03:12.526830+00:00"},{"alias_kind":"pith_short_12","alias_value":"7WJIT32GWQ62","created_at":"2026-07-05T05:03:12.526830+00:00"},{"alias_kind":"pith_short_16","alias_value":"7WJIT32GWQ62ZHQO","created_at":"2026-07-05T05:03:12.526830+00:00"},{"alias_kind":"pith_short_8","alias_value":"7WJIT32G","created_at":"2026-07-05T05:03:12.526830+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.13411","citing_title":"Aligning Knowledge Graphs and Language Models for Factual Accuracy","ref_index":33,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7WJIT32GWQ62ZHQORUYEXLEWKF","json":"https://pith.science/pith/7WJIT32GWQ62ZHQORUYEXLEWKF.json","graph_json":"https://pith.science/api/pith-number/7WJIT32GWQ62ZHQORUYEXLEWKF/graph.json","events_json":"https://pith.science/api/pith-number/7WJIT32GWQ62ZHQORUYEXLEWKF/events.json","paper":"https://pith.science/paper/7WJIT32G"},"agent_actions":{"view_html":"https://pith.science/pith/7WJIT32GWQ62ZHQORUYEXLEWKF","download_json":"https://pith.science/pith/7WJIT32GWQ62ZHQORUYEXLEWKF.json","view_paper":"https://pith.science/paper/7WJIT32G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.01613&json=true","fetch_graph":"https://pith.science/api/pith-number/7WJIT32GWQ62ZHQORUYEXLEWKF/graph.json","fetch_events":"https://pith.science/api/pith-number/7WJIT32GWQ62ZHQORUYEXLEWKF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7WJIT32GWQ62ZHQORUYEXLEWKF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7WJIT32GWQ62ZHQORUYEXLEWKF/action/storage_attestation","attest_author":"https://pith.science/pith/7WJIT32GWQ62ZHQORUYEXLEWKF/action/author_attestation","sign_citation":"https://pith.science/pith/7WJIT32GWQ62ZHQORUYEXLEWKF/action/citation_signature","submit_replication":"https://pith.science/pith/7WJIT32GWQ62ZHQORUYEXLEWKF/action/replication_record"}},"created_at":"2026-07-05T05:03:12.526830+00:00","updated_at":"2026-07-05T05:03:12.526830+00:00"}