Triple

T16430134
Position Surface form Disambiguated ID Type / Status
Subject Gelb E399049 entity
Predicate hasNotableBearer P458 FINISHED
Object Peter Gelb E88900 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: Peter Gelb | Statement: [Gelb, hasNotableBearer, Peter Gelb]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Gelb
Context triple: [Gelb, hasNotableBearer, Peter Gelb]
  • A. Peter Gelb chosen
    Peter Gelb is an American arts administrator best known for serving as the general manager of New York’s Metropolitan Opera, where he has led efforts to modernize productions and expand the company’s global reach.
  • B. James S. Levine
    James S. Levine is an American television composer known for scoring numerous popular TV series, including crime and drama shows.
  • C. Harry Nederlander
    Harry Nederlander is a member of the prominent Nederlander family known for its major role in American theater ownership and production.
  • D. Robert Nederlander
    Robert Nederlander is an American theater owner and producer, known as a member of the prominent Nederlander family that operates major Broadway and live-entertainment venues.
  • E. Ned Nederlander
    Ned Nederlander is one of the three bumbling silent-film stars in the comedy film "Three Amigos," portrayed by Martin Short.
  • 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_69d87f2b9024819085c20e52de95d583 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e328fe0f488190ac34aa677c980a20 completed April 18, 2026, 6:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a00458331748190a4bd1c5d2d466e6d completed May 10, 2026, 8:44 a.m.
Created at: April 10, 2026, 5:10 a.m.