Triple

T9698363
Position Surface form Disambiguated ID Type / Status
Subject Elaine de Kooning E234710 entity
Predicate employer P7 FINISHED
Object Carnegie Mellon University E33793 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: Carnegie Mellon University | Statement: [Elaine de Kooning, employer, Carnegie Mellon University]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Carnegie Mellon University
Context triple: [Elaine de Kooning, employer, Carnegie Mellon University]
  • A. CMU
    CMU is a public university in Grand Junction, Colorado, known for its diverse undergraduate programs and strong regional presence on the Western Slope.
  • B. CMU
    CMU is a major medical university located in Shenyang, China, known for its education and research in clinical medicine and related health sciences.
  • C. CMU
    CMU is a major public research university in Chiang Mai, Thailand, known for its comprehensive academic programs and role as a leading educational institution in northern Thailand.
  • D. CMU chosen
    CMU is a private research university in Pittsburgh, Pennsylvania, renowned for its leading programs in computer science, engineering, and the arts.
  • E. University of Pittsburgh
    The University of Pittsburgh is a major public research university in Pittsburgh, Pennsylvania, known for its strong programs in medicine, engineering, and the liberal arts.
  • 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9d3d425c8190b652d84186b5ce9f completed April 1, 2026, 10:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69d19125d3cc819082b3736d1b4ba802 completed April 4, 2026, 10:31 p.m.
Created at: March 30, 2026, 8:18 p.m.