{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ISBRZLKM6LBOTGEUIMCFENHJR7","short_pith_number":"pith:ISBRZLKM","canonical_record":{"source":{"id":"2307.09909","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-07-19T11:25:12Z","cross_cats_sorted":[],"title_canon_sha256":"4fa63d961fe5364617e3b97d83590d9e88cbe62d8968cc9c5fe56ee96842a051","abstract_canon_sha256":"9acb7f40b48f77c28f63c090a02f53c65613ef7d0b85f4db775d1cb5a3552a39"},"schema_version":"1.0"},"canonical_sha256":"44831cad4cf2c2e9989443045234e98fc7ea044a68d9311fd52a61846f05ceb6","source":{"kind":"arxiv","id":"2307.09909","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.09909","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"arxiv_version","alias_value":"2307.09909v1","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09909","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"pith_short_12","alias_value":"ISBRZLKM6LBO","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"pith_short_16","alias_value":"ISBRZLKM6LBOTGEU","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"pith_short_8","alias_value":"ISBRZLKM","created_at":"2026-07-05T06:32:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ISBRZLKM6LBOTGEUIMCFENHJR7","target":"record","payload":{"canonical_record":{"source":{"id":"2307.09909","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-07-19T11:25:12Z","cross_cats_sorted":[],"title_canon_sha256":"4fa63d961fe5364617e3b97d83590d9e88cbe62d8968cc9c5fe56ee96842a051","abstract_canon_sha256":"9acb7f40b48f77c28f63c090a02f53c65613ef7d0b85f4db775d1cb5a3552a39"},"schema_version":"1.0"},"canonical_sha256":"44831cad4cf2c2e9989443045234e98fc7ea044a68d9311fd52a61846f05ceb6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:32:47.956641Z","signature_b64":"bGvILaXf32jWTNFKxUttacIYK80YFrTIcI+f7FWWkknOrZuYt+JX8piWDYwAY4TVmMvLvDTvW5dBRvlY56TGCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44831cad4cf2c2e9989443045234e98fc7ea044a68d9311fd52a61846f05ceb6","last_reissued_at":"2026-07-05T06:32:47.956258Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:32:47.956258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.09909","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":"2026-07-05T06:32:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"whtz4dBLCRWzRGt3XPjNunA/KGCN72spDy4dyBT+jreV/qUPkwbHCYnd5EFVV0Fj9nahSIAddQMqdnTTY+d1AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T07:30:58.937911Z"},"content_sha256":"e48891d299a9becf00e6714ba3b669162592c0621761f63af59c1835a524364a","schema_version":"1.0","event_id":"sha256:e48891d299a9becf00e6714ba3b669162592c0621761f63af59c1835a524364a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ISBRZLKM6LBOTGEUIMCFENHJR7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Chit-Chat or Deep Talk: Prompt Engineering for Process Mining","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dirk Fahland, Michal Sroka, Urszula Jessen","submitted_at":"2023-07-19T11:25:12Z","abstract_excerpt":"This research investigates the application of Large Language Models (LLMs) to augment conversational agents in process mining, aiming to tackle its inherent complexity and diverse skill requirements. While LLM advancements present novel opportunities for conversational process mining, generating efficient outputs is still a hurdle. We propose an innovative approach that amend many issues in existing solutions, informed by prior research on Natural Language Processing (NLP) for conversational agents. Leveraging LLMs, our framework improves both accessibility and agent performance, as demonstrat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09909","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/2307.09909/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":"2026-07-05T06:32:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+N9a/FYbkRDGzVf/JYHQaHwf50E7w9nJwRf9CKorAgbMFCSPs6SE7SR66LpyuBsWLeauTgbm2GhENMOWjiwNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T07:30:58.938838Z"},"content_sha256":"df3f1619e483c7c16fdf0784ded4c7a0fb6eb812e6a9df8ba0ab4a0de9bf74d8","schema_version":"1.0","event_id":"sha256:df3f1619e483c7c16fdf0784ded4c7a0fb6eb812e6a9df8ba0ab4a0de9bf74d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ISBRZLKM6LBOTGEUIMCFENHJR7/bundle.json","state_url":"https://pith.science/pith/ISBRZLKM6LBOTGEUIMCFENHJR7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ISBRZLKM6LBOTGEUIMCFENHJR7/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":"2026-08-17T07:30:58Z","links":{"resolver":"https://pith.science/pith/ISBRZLKM6LBOTGEUIMCFENHJR7","bundle":"https://pith.science/pith/ISBRZLKM6LBOTGEUIMCFENHJR7/bundle.json","state":"https://pith.science/pith/ISBRZLKM6LBOTGEUIMCFENHJR7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ISBRZLKM6LBOTGEUIMCFENHJR7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ISBRZLKM6LBOTGEUIMCFENHJR7","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":"9acb7f40b48f77c28f63c090a02f53c65613ef7d0b85f4db775d1cb5a3552a39","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-07-19T11:25:12Z","title_canon_sha256":"4fa63d961fe5364617e3b97d83590d9e88cbe62d8968cc9c5fe56ee96842a051"},"schema_version":"1.0","source":{"id":"2307.09909","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.09909","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"arxiv_version","alias_value":"2307.09909v1","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09909","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"pith_short_12","alias_value":"ISBRZLKM6LBO","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"pith_short_16","alias_value":"ISBRZLKM6LBOTGEU","created_at":"2026-07-05T06:32:47Z"},{"alias_kind":"pith_short_8","alias_value":"ISBRZLKM","created_at":"2026-07-05T06:32:47Z"}],"graph_snapshots":[{"event_id":"sha256:df3f1619e483c7c16fdf0784ded4c7a0fb6eb812e6a9df8ba0ab4a0de9bf74d8","target":"graph","created_at":"2026-07-05T06:32:47Z","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/2307.09909/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This research investigates the application of Large Language Models (LLMs) to augment conversational agents in process mining, aiming to tackle its inherent complexity and diverse skill requirements. While LLM advancements present novel opportunities for conversational process mining, generating efficient outputs is still a hurdle. We propose an innovative approach that amend many issues in existing solutions, informed by prior research on Natural Language Processing (NLP) for conversational agents. Leveraging LLMs, our framework improves both accessibility and agent performance, as demonstrat","authors_text":"Dirk Fahland, Michal Sroka, Urszula Jessen","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-07-19T11:25:12Z","title":"Chit-Chat or Deep Talk: Prompt Engineering for Process Mining"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09909","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:e48891d299a9becf00e6714ba3b669162592c0621761f63af59c1835a524364a","target":"record","created_at":"2026-07-05T06:32:47Z","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":"9acb7f40b48f77c28f63c090a02f53c65613ef7d0b85f4db775d1cb5a3552a39","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-07-19T11:25:12Z","title_canon_sha256":"4fa63d961fe5364617e3b97d83590d9e88cbe62d8968cc9c5fe56ee96842a051"},"schema_version":"1.0","source":{"id":"2307.09909","kind":"arxiv","version":1}},"canonical_sha256":"44831cad4cf2c2e9989443045234e98fc7ea044a68d9311fd52a61846f05ceb6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"44831cad4cf2c2e9989443045234e98fc7ea044a68d9311fd52a61846f05ceb6","first_computed_at":"2026-07-05T06:32:47.956258Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:32:47.956258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bGvILaXf32jWTNFKxUttacIYK80YFrTIcI+f7FWWkknOrZuYt+JX8piWDYwAY4TVmMvLvDTvW5dBRvlY56TGCA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:32:47.956641Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.09909","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e48891d299a9becf00e6714ba3b669162592c0621761f63af59c1835a524364a","sha256:df3f1619e483c7c16fdf0784ded4c7a0fb6eb812e6a9df8ba0ab4a0de9bf74d8"],"state_sha256":"839940a5cda876aa5572916c8bc60fe29dd2952d79945da3d670855876608bfb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jdRK+gf50emotZ93JEnQ5qZ7RJbZULKhi+FCPjJcJXBWLIKj1DiOEq6QbiejHIkRRV2n9ITKzH6+fj1QSykQAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T07:30:58.958343Z","bundle_sha256":"a6fb6a2982ef95affbe3b48344b698538bb4c4ccfefc5b9fc7752ce75b125767"}}