{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:4DJD624WATIEJBRAKVWT2YM2RX","short_pith_number":"pith:4DJD624W","canonical_record":{"source":{"id":"2310.14174","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T04:19:17Z","cross_cats_sorted":[],"title_canon_sha256":"0e6f414d99a559ea82d5ebc527fbe9d146d87ee8d9c1d37df88243dca1665fa5","abstract_canon_sha256":"05d52bff2611f6543e7ac2e07407ffed2c71504c85dbe8cb27a75fbfb7f286a4"},"schema_version":"1.0"},"canonical_sha256":"e0d23f6b9604d0448620556d3d619a8df840e71f4799cd877e33604556188c02","source":{"kind":"arxiv","id":"2310.14174","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14174","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14174v2","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14174","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"pith_short_12","alias_value":"4DJD624WATIE","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"pith_short_16","alias_value":"4DJD624WATIEJBRA","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"pith_short_8","alias_value":"4DJD624W","created_at":"2026-07-05T07:43:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:4DJD624WATIEJBRAKVWT2YM2RX","target":"record","payload":{"canonical_record":{"source":{"id":"2310.14174","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T04:19:17Z","cross_cats_sorted":[],"title_canon_sha256":"0e6f414d99a559ea82d5ebc527fbe9d146d87ee8d9c1d37df88243dca1665fa5","abstract_canon_sha256":"05d52bff2611f6543e7ac2e07407ffed2c71504c85dbe8cb27a75fbfb7f286a4"},"schema_version":"1.0"},"canonical_sha256":"e0d23f6b9604d0448620556d3d619a8df840e71f4799cd877e33604556188c02","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:43:33.811265Z","signature_b64":"5kPND8ny/RzqCa5k5/uQZk1c2UfBu3pdSgOJiNmmJGKBgeFKUzI1S1/IN+HRf9yrsjJCyIb9ekuACJxkCrEiBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0d23f6b9604d0448620556d3d619a8df840e71f4799cd877e33604556188c02","last_reissued_at":"2026-07-05T07:43:33.809218Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:43:33.809218Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.14174","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:43:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QfLCvPgWZQLAVYMmFpV1+qnDjOGEFw6pzFBQZcOqrwRdMZt+6TxNa5vUnMKh2zifQ+ze50Uyu4T9iNwHY458Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:42:42.158189Z"},"content_sha256":"89f7280c8c46b8c9ea4a003946e33bb921ea82384950e17b03149dd44e211c64","schema_version":"1.0","event_id":"sha256:89f7280c8c46b8c9ea4a003946e33bb921ea82384950e17b03149dd44e211c64"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:4DJD624WATIEJBRAKVWT2YM2RX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An In-Context Schema Understanding Method for Knowledge Base Question Answering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiafeng Guo, Long Bai, Saiping Guan, Xiaolong Jin, Xueqi Cheng, Yantao Liu, Yucan Guo, Zixuan Li","submitted_at":"2023-10-22T04:19:17Z","abstract_excerpt":"The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. Recently, Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. In doing so, a major challenge for LLMs is to overcome the immensity and heterogeneity of knowledge base schemas.Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details.Then, an extra module is used to inject schema information to these drafts.In contrast, in this p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14174","kind":"arxiv","version":2},"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/2310.14174/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:43:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KvacwxvgBLsYAAhK0s+M2N1Hl8b09ydv3p67pyK86LsIfhvve/11uVktdDa1bly0QsLGdM5SUL+5eKGtmNX8CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:42:42.158580Z"},"content_sha256":"573c787af7bd85fc3b76afa0f23ce744df0f82368706515218a07c43d78ffafb","schema_version":"1.0","event_id":"sha256:573c787af7bd85fc3b76afa0f23ce744df0f82368706515218a07c43d78ffafb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4DJD624WATIEJBRAKVWT2YM2RX/bundle.json","state_url":"https://pith.science/pith/4DJD624WATIEJBRAKVWT2YM2RX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4DJD624WATIEJBRAKVWT2YM2RX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T13:42:42Z","links":{"resolver":"https://pith.science/pith/4DJD624WATIEJBRAKVWT2YM2RX","bundle":"https://pith.science/pith/4DJD624WATIEJBRAKVWT2YM2RX/bundle.json","state":"https://pith.science/pith/4DJD624WATIEJBRAKVWT2YM2RX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4DJD624WATIEJBRAKVWT2YM2RX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:4DJD624WATIEJBRAKVWT2YM2RX","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":"05d52bff2611f6543e7ac2e07407ffed2c71504c85dbe8cb27a75fbfb7f286a4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T04:19:17Z","title_canon_sha256":"0e6f414d99a559ea82d5ebc527fbe9d146d87ee8d9c1d37df88243dca1665fa5"},"schema_version":"1.0","source":{"id":"2310.14174","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.14174","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"arxiv_version","alias_value":"2310.14174v2","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.14174","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"pith_short_12","alias_value":"4DJD624WATIE","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"pith_short_16","alias_value":"4DJD624WATIEJBRA","created_at":"2026-07-05T07:43:33Z"},{"alias_kind":"pith_short_8","alias_value":"4DJD624W","created_at":"2026-07-05T07:43:33Z"}],"graph_snapshots":[{"event_id":"sha256:573c787af7bd85fc3b76afa0f23ce744df0f82368706515218a07c43d78ffafb","target":"graph","created_at":"2026-07-05T07:43:33Z","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/2310.14174/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. Recently, Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. In doing so, a major challenge for LLMs is to overcome the immensity and heterogeneity of knowledge base schemas.Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details.Then, an extra module is used to inject schema information to these drafts.In contrast, in this p","authors_text":"Jiafeng Guo, Long Bai, Saiping Guan, Xiaolong Jin, Xueqi Cheng, Yantao Liu, Yucan Guo, Zixuan Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T04:19:17Z","title":"An In-Context Schema Understanding Method for Knowledge Base Question Answering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.14174","kind":"arxiv","version":2},"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":"sha256:89f7280c8c46b8c9ea4a003946e33bb921ea82384950e17b03149dd44e211c64","target":"record","created_at":"2026-07-05T07:43:33Z","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":"05d52bff2611f6543e7ac2e07407ffed2c71504c85dbe8cb27a75fbfb7f286a4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-22T04:19:17Z","title_canon_sha256":"0e6f414d99a559ea82d5ebc527fbe9d146d87ee8d9c1d37df88243dca1665fa5"},"schema_version":"1.0","source":{"id":"2310.14174","kind":"arxiv","version":2}},"canonical_sha256":"e0d23f6b9604d0448620556d3d619a8df840e71f4799cd877e33604556188c02","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e0d23f6b9604d0448620556d3d619a8df840e71f4799cd877e33604556188c02","first_computed_at":"2026-07-05T07:43:33.809218Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:43:33.809218Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5kPND8ny/RzqCa5k5/uQZk1c2UfBu3pdSgOJiNmmJGKBgeFKUzI1S1/IN+HRf9yrsjJCyIb9ekuACJxkCrEiBg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:43:33.811265Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.14174","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:89f7280c8c46b8c9ea4a003946e33bb921ea82384950e17b03149dd44e211c64","sha256:573c787af7bd85fc3b76afa0f23ce744df0f82368706515218a07c43d78ffafb"],"state_sha256":"b8c68d0a455b02c77cc592873abd02bd36516aad5b90691b14c03780923704f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bu5UZmnd3LE7wp78g5hNkI7QafNJG5iGQiPlY0k4BP8yoziIzFprkmi+qPXxnjaW9ObxbX+4wadzs3D6+Lv3Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:42:42.160572Z","bundle_sha256":"4aebb899f2fef8dbd0ab1ef47f3a6acc037fb460a0fd660453570ad1db8ba7ae"}}