Triple

T2895281
Position Surface form Disambiguated ID Type / Status
Subject Corpse Bride E63924 entity
Predicate cinematographyBy P1953 FINISHED
Object Pete Kozachik E315878 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: Pete Kozachik | Statement: [Corpse Bride, cinematographyBy, Pete Kozachik]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pete Kozachik
Context triple: [Corpse Bride, cinematographyBy, Pete Kozachik]
  • A. Pete Kozachik chosen
    Pete Kozachik is an American cinematographer best known for his work on stop-motion animated films, including the cult classic "The Nightmare Before Christmas."
  • B. Bernie Federko
    Bernie Federko is a Hall of Fame Canadian center best known as a longtime offensive star and playmaker for the St. Louis Blues in the NHL.
  • C. Tony Kubek
    Tony Kubek is a former Major League Baseball shortstop and longtime television broadcaster best known for his work as a color commentator on national baseball telecasts.
  • D. Michael Kuzak
    Michael Kuzak is a central attorney character on the television legal drama "L.A. Law," known for his idealism and high-profile courtroom battles.
  • E. Wayne Chrebet
    Wayne Chrebet is a former NFL wide receiver best known for his productive career with the New York Jets as an undersized but clutch possession receiver.
  • 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_69ab4c45822c8190830c5f2bb97bcfd0 completed March 6, 2026, 9:51 p.m.
NER Named-entity recognition batch_69abe06509808190b673222b9ae3d599 completed March 7, 2026, 8:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69b12e1250348190abeac8ef6dd9d916 completed March 11, 2026, 8:55 a.m.
Created at: March 6, 2026, 10:08 p.m.