Triple

T7342113
Position Surface form Disambiguated ID Type / Status
Subject Cliff Illig E169278 entity
Predicate coFounded P104 FINISHED
Object Cerner Corporation E20635 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: Cerner Corporation | Statement: [Cliff Illig, coFounded, Cerner Corporation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cerner Corporation
Context triple: [Cliff Illig, coFounded, Cerner Corporation]
  • A. Cerner chosen
    Cerner is a major American health information technology company best known for its electronic health record (EHR) systems and healthcare data solutions.
  • B. 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.
  • C. HCA Healthcare
    HCA Healthcare is a large American for-profit healthcare company that operates a nationwide network of hospitals and healthcare facilities.
  • D. NextGen Healthcare
    NextGen Healthcare is a U.S.-based health IT company that provides electronic health record, practice management, and related software solutions for medical practices and healthcare organizations.
  • E. 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.
  • 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_69c68a57710481909f0c1f3c6ebdb6f2 completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f0da176081909a40552c6f22087c completed March 27, 2026, 9:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7fa867b7881908590ca1ea195b1b5 completed March 28, 2026, 3:57 p.m.
Created at: March 27, 2026, 3:04 p.m.