Triple

T4043895
Position Surface form Disambiguated ID Type / Status
Subject Jeff Immelt E84016 entity
Predicate boardMemberOf P10 FINISHED
Object Athenahealth E114174 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Athenahealth | Statement: [Jeff Immelt, boardMemberOf, Athenahealth]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Athenahealth
Context triple: [Jeff Immelt, boardMemberOf, Athenahealth]
  • A. Athenahealth chosen
    Athenahealth is a U.S.-based healthcare technology company that provides cloud-based electronic health record, practice management, and revenue cycle management solutions for medical practices and health systems.
  • B. Flatiron Health
    Flatiron Health is a healthcare technology company that specializes in using real-world oncology data and software to improve cancer care and research.
  • C. Allscripts
    Allscripts is a healthcare information technology company known for providing electronic health record (EHR), practice management, and related software solutions to hospitals and physician practices.
  • D. Cerner
    Cerner is a major American health information technology company best known for its electronic health record (EHR) systems and healthcare data solutions.
  • E. Epic Systems
    Epic Systems is a leading American healthcare software company best known for its widely used electronic health record (EHR) systems in hospitals and clinics.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69aed930bd5c819083e7dcc14fc44f69 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefb5d759c8190b61fbbe94ffe2bf7 completed March 9, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5564fb54c81909f40ca1d6f1e521e completed March 14, 2026, 12:36 p.m.
Created at: March 9, 2026, 3:37 p.m.