{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:36DFWUF72VLS7S66MQPVWFZBHM","short_pith_number":"pith:36DFWUF7","canonical_record":{"source":{"id":"1810.05682","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-12T19:01:32Z","cross_cats_sorted":[],"title_canon_sha256":"87b1f7951174caeab8a0739fc281299480e40092e09a87f0b26f50ad9caeba92","abstract_canon_sha256":"a08e8f04147ec27d048aecaab1fb3895cf1a1d9c4c47d6368f0089319f8a4b45"},"schema_version":"1.0"},"canonical_sha256":"df865b50bfd5572fcbde641f5b17213b12178709d0e5b68a2c807a932139e828","source":{"kind":"arxiv","id":"1810.05682","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.05682","created_at":"2026-05-18T00:03:27Z"},{"alias_kind":"arxiv_version","alias_value":"1810.05682v1","created_at":"2026-05-18T00:03:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.05682","created_at":"2026-05-18T00:03:27Z"},{"alias_kind":"pith_short_12","alias_value":"36DFWUF72VLS","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"36DFWUF72VLS7S66","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"36DFWUF7","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:36DFWUF72VLS7S66MQPVWFZBHM","target":"record","payload":{"canonical_record":{"source":{"id":"1810.05682","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-12T19:01:32Z","cross_cats_sorted":[],"title_canon_sha256":"87b1f7951174caeab8a0739fc281299480e40092e09a87f0b26f50ad9caeba92","abstract_canon_sha256":"a08e8f04147ec27d048aecaab1fb3895cf1a1d9c4c47d6368f0089319f8a4b45"},"schema_version":"1.0"},"canonical_sha256":"df865b50bfd5572fcbde641f5b17213b12178709d0e5b68a2c807a932139e828","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:27.260483Z","signature_b64":"rEjcEzxUqylcHYH2NhFapsdqXIoeRvlzHh0MVMmNfA2e9GZf1HToYAwaodb1pWfNefPO2ub+oxXIlXUv/ezHAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df865b50bfd5572fcbde641f5b17213b12178709d0e5b68a2c807a932139e828","last_reissued_at":"2026-05-18T00:03:27.260016Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:27.260016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.05682","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:03:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eCMRwRBbdpvsNV850rmdPJINIabM4WbEC3R+mwe2YnMV4FdRg/kWWY8X2+6pVRNKy4r6HzwfoXUqXJUSu2+VAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T09:43:22.114587Z"},"content_sha256":"490cf766cf616c4aa7ece56c15948a739163cba52af6e7a603fa7af60dd52d26","schema_version":"1.0","event_id":"sha256:490cf766cf616c4aa7ece56c15948a739163cba52af6e7a603fa7af60dd52d26"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:36DFWUF72VLS7S66MQPVWFZBHM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Adam Trischler, Andrew McCallum, Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan","submitted_at":"2018-10-12T19:01:32Z","abstract_excerpt":"We propose a neural machine-reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure, and uses them to track the evolving states of participant entities. We harness and extend a recently proposed machine reading comprehension (MRC) model to query for entity states, since these states are generally communicated in spans of text and MRC models perform well in extracting entity-centric spans. The explicit, structured, and evolving knowledge graph representations that our model constructs can be used in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.05682","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":""},"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":"2026-05-18T00:03:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vJy+qjOq2jfkVSjGGZiDNG07Q/vOLqDSZyw/hK/NhzKzwpVkoyPRLkPJpiPZZ/U1+veVxTdfUN1Ju9LaAKCzCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T09:43:22.115260Z"},"content_sha256":"4f6b7c4609b33597ce810dd35eea4a2f0ee41658229fbed21c5f25ce1ad456ab","schema_version":"1.0","event_id":"sha256:4f6b7c4609b33597ce810dd35eea4a2f0ee41658229fbed21c5f25ce1ad456ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/36DFWUF72VLS7S66MQPVWFZBHM/bundle.json","state_url":"https://pith.science/pith/36DFWUF72VLS7S66MQPVWFZBHM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/36DFWUF72VLS7S66MQPVWFZBHM/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":"2026-05-26T09:43:22Z","links":{"resolver":"https://pith.science/pith/36DFWUF72VLS7S66MQPVWFZBHM","bundle":"https://pith.science/pith/36DFWUF72VLS7S66MQPVWFZBHM/bundle.json","state":"https://pith.science/pith/36DFWUF72VLS7S66MQPVWFZBHM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/36DFWUF72VLS7S66MQPVWFZBHM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:36DFWUF72VLS7S66MQPVWFZBHM","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":"a08e8f04147ec27d048aecaab1fb3895cf1a1d9c4c47d6368f0089319f8a4b45","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-12T19:01:32Z","title_canon_sha256":"87b1f7951174caeab8a0739fc281299480e40092e09a87f0b26f50ad9caeba92"},"schema_version":"1.0","source":{"id":"1810.05682","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.05682","created_at":"2026-05-18T00:03:27Z"},{"alias_kind":"arxiv_version","alias_value":"1810.05682v1","created_at":"2026-05-18T00:03:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.05682","created_at":"2026-05-18T00:03:27Z"},{"alias_kind":"pith_short_12","alias_value":"36DFWUF72VLS","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"36DFWUF72VLS7S66","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"36DFWUF7","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:4f6b7c4609b33597ce810dd35eea4a2f0ee41658229fbed21c5f25ce1ad456ab","target":"graph","created_at":"2026-05-18T00:03:27Z","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"},"paper":{"abstract_excerpt":"We propose a neural machine-reading model that constructs dynamic knowledge graphs from procedural text. It builds these graphs recurrently for each step of the described procedure, and uses them to track the evolving states of participant entities. We harness and extend a recently proposed machine reading comprehension (MRC) model to query for entity states, since these states are generally communicated in spans of text and MRC models perform well in extracting entity-centric spans. The explicit, structured, and evolving knowledge graph representations that our model constructs can be used in","authors_text":"Adam Trischler, Andrew McCallum, Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-12T19:01:32Z","title":"Building Dynamic Knowledge Graphs from Text using Machine Reading Comprehension"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.05682","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":"sha256:490cf766cf616c4aa7ece56c15948a739163cba52af6e7a603fa7af60dd52d26","target":"record","created_at":"2026-05-18T00:03:27Z","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":"a08e8f04147ec27d048aecaab1fb3895cf1a1d9c4c47d6368f0089319f8a4b45","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-10-12T19:01:32Z","title_canon_sha256":"87b1f7951174caeab8a0739fc281299480e40092e09a87f0b26f50ad9caeba92"},"schema_version":"1.0","source":{"id":"1810.05682","kind":"arxiv","version":1}},"canonical_sha256":"df865b50bfd5572fcbde641f5b17213b12178709d0e5b68a2c807a932139e828","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df865b50bfd5572fcbde641f5b17213b12178709d0e5b68a2c807a932139e828","first_computed_at":"2026-05-18T00:03:27.260016Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:03:27.260016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rEjcEzxUqylcHYH2NhFapsdqXIoeRvlzHh0MVMmNfA2e9GZf1HToYAwaodb1pWfNefPO2ub+oxXIlXUv/ezHAw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:03:27.260483Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.05682","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:490cf766cf616c4aa7ece56c15948a739163cba52af6e7a603fa7af60dd52d26","sha256:4f6b7c4609b33597ce810dd35eea4a2f0ee41658229fbed21c5f25ce1ad456ab"],"state_sha256":"c4dcc6e4f4e474431b52f5fcdc5ae83fa3b38612dfd20e430f6e3ab6557e0402"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ue3hBm4bOr39QaZMXuX3JDvSLwoz6Va/UqT0pxGo8WCmxkzwxz3m+e2SoQ/xv+TME0sVEjwHOLuGe+3IDjKLBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T09:43:22.118820Z","bundle_sha256":"2561555d2c893ff2f08f6c5e6de7a7f19130bf42cf5cd48128145a2550275b24"}}