Triple

T13702739
Position Surface form Disambiguated ID Type / Status
Subject Fort Apache, The Bronx E328559 entity
Predicate musicBy P1952 FINISHED
Object Jonathan Tunick E571432 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: Jonathan Tunick | Statement: [Fort Apache, The Bronx, musicBy, Jonathan Tunick]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jonathan Tunick
Context triple: [Fort Apache, The Bronx, musicBy, Jonathan Tunick]
  • A. Jonathan Tunick chosen
    Jonathan Tunick is a renowned American orchestrator, musical director, and composer best known for his long-standing collaboration with Stephen Sondheim on numerous Broadway productions.
  • B. Alan Sytner
    Alan Sytner was a British nightclub owner and impresario best known for creating Liverpool’s iconic Cavern Club, which became a pivotal venue in the rise of The Beatles and the Merseybeat scene.
  • C. Michele Moerth
    Michele Moerth is best known as the wife of the late British actor Ben Cross, recognized for his role in the film "Chariots of Fire."
  • D. James Lindenbaum
    James Lindenbaum is a technology entrepreneur best known as a co-founder of the cloud platform-as-a-service company Heroku.
  • E. Andrew Shulkind
    Andrew Shulkind is a cinematographer known for his atmospheric and visually immersive work in genre films and television.
  • 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_69d8076ff62081908a7bd79889edd7a0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dcad162158819089280ee1e6b5c2cf completed April 13, 2026, 8:45 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b8cae6f081908145b6cd4c0ba53c completed May 3, 2026, 9:06 p.m.
Created at: April 9, 2026, 9:54 p.m.