{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:24UQOMZLP2KBHZRJHO6JKGUQW4","short_pith_number":"pith:24UQOMZL","schema_version":"1.0","canonical_sha256":"d72907332b7e9413e6293bbc951a90b71c2ae9bd9716fd803c884867187b59f9","source":{"kind":"arxiv","id":"2307.09923","version":1},"attestation_state":"computed","paper":{"title":"Large Language Models can accomplish Business Process Management Tasks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jana-Rebecca Rehse, Luka Abb, Michael Grohs, Nourhan Elsayed","submitted_at":"2023-07-19T11:54:46Z","abstract_excerpt":"Business Process Management (BPM) aims to improve organizational activities and their outcomes by managing the underlying processes. To achieve this, it is often necessary to consider information from various sources, including unstructured textual documents. Therefore, researchers have developed several BPM-specific solutions that extract information from textual documents using Natural Language Processing techniques. These solutions are specific to their respective tasks and cannot accomplish multiple process-related problems as a general-purpose instrument. However, in light of the recent e"},"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":"2307.09923","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-19T11:54:46Z","cross_cats_sorted":[],"title_canon_sha256":"0be631ab805ec57606b1d71a8d29257de81e545566e5d3b2f54bb4ca248637c8","abstract_canon_sha256":"71573eeaab7d70e6b453596ac02ad706180c978f02ef3052622608214f0617f2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:32:48.029475Z","signature_b64":"lMOuLvvqnXiMW2hxVWLIUWhxDKlIolmeUGYDognmecoxuh2goU1xpTAwulRDC67nz6i60hoXFd/vHoHgpbeEAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d72907332b7e9413e6293bbc951a90b71c2ae9bd9716fd803c884867187b59f9","last_reissued_at":"2026-07-05T06:32:48.029059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:32:48.029059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Large Language Models can accomplish Business Process Management Tasks","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jana-Rebecca Rehse, Luka Abb, Michael Grohs, Nourhan Elsayed","submitted_at":"2023-07-19T11:54:46Z","abstract_excerpt":"Business Process Management (BPM) aims to improve organizational activities and their outcomes by managing the underlying processes. To achieve this, it is often necessary to consider information from various sources, including unstructured textual documents. Therefore, researchers have developed several BPM-specific solutions that extract information from textual documents using Natural Language Processing techniques. These solutions are specific to their respective tasks and cannot accomplish multiple process-related problems as a general-purpose instrument. However, in light of the recent e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09923","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/2307.09923/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":"2307.09923","created_at":"2026-07-05T06:32:48.029120+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.09923v1","created_at":"2026-07-05T06:32:48.029120+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09923","created_at":"2026-07-05T06:32:48.029120+00:00"},{"alias_kind":"pith_short_12","alias_value":"24UQOMZLP2KB","created_at":"2026-07-05T06:32:48.029120+00:00"},{"alias_kind":"pith_short_16","alias_value":"24UQOMZLP2KBHZRJ","created_at":"2026-07-05T06:32:48.029120+00:00"},{"alias_kind":"pith_short_8","alias_value":"24UQOMZL","created_at":"2026-07-05T06:32:48.029120+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.09489","citing_title":"LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22502","citing_title":"Compiling Agentic Workflows into LLM Weights: Near-Frontier Quality at Two Orders of Magnitude Less Cost","ref_index":75,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/24UQOMZLP2KBHZRJHO6JKGUQW4","json":"https://pith.science/pith/24UQOMZLP2KBHZRJHO6JKGUQW4.json","graph_json":"https://pith.science/api/pith-number/24UQOMZLP2KBHZRJHO6JKGUQW4/graph.json","events_json":"https://pith.science/api/pith-number/24UQOMZLP2KBHZRJHO6JKGUQW4/events.json","paper":"https://pith.science/paper/24UQOMZL"},"agent_actions":{"view_html":"https://pith.science/pith/24UQOMZLP2KBHZRJHO6JKGUQW4","download_json":"https://pith.science/pith/24UQOMZLP2KBHZRJHO6JKGUQW4.json","view_paper":"https://pith.science/paper/24UQOMZL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.09923&json=true","fetch_graph":"https://pith.science/api/pith-number/24UQOMZLP2KBHZRJHO6JKGUQW4/graph.json","fetch_events":"https://pith.science/api/pith-number/24UQOMZLP2KBHZRJHO6JKGUQW4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/24UQOMZLP2KBHZRJHO6JKGUQW4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/24UQOMZLP2KBHZRJHO6JKGUQW4/action/storage_attestation","attest_author":"https://pith.science/pith/24UQOMZLP2KBHZRJHO6JKGUQW4/action/author_attestation","sign_citation":"https://pith.science/pith/24UQOMZLP2KBHZRJHO6JKGUQW4/action/citation_signature","submit_replication":"https://pith.science/pith/24UQOMZLP2KBHZRJHO6JKGUQW4/action/replication_record"}},"created_at":"2026-07-05T06:32:48.029120+00:00","updated_at":"2026-07-05T06:32:48.029120+00:00"}