Triple

T1987514
Position Surface form Disambiguated ID Type / Status
Subject The Lorax (2012 film) E43174 entity
Predicate musicBy P1952 FINISHED
Object John Powell E74988 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: John Powell | Statement: [The Lorax (2012 film), musicBy, John Powell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Powell
Context triple: [The Lorax (2012 film), musicBy, John Powell]
  • A. John Powell chosen
    John Powell is a British-born, Academy Award–nominated film composer renowned for his dynamic scores for animated and action films such as the "How to Train Your Dragon" series and the "Bourne" franchise.
  • B. John Debney
    John Debney is an American film composer known for scoring a wide range of movies and television shows, including major studio productions and acclaimed dramas.
  • C. James Newton Howard
    James Newton Howard is an acclaimed American composer best known for his prolific film and television scores across a wide range of genres.
  • D. Michael Abels
    Michael Abels is an American composer best known for his innovative, genre-blending film scores for Jordan Peele’s movies, including Get Out, Us, and Nope.
  • E. Steve Jablonsky
    Steve Jablonsky is an American composer best known for his film and television scores, particularly for the Transformers franchise and numerous action and science-fiction projects.
  • 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_69a88713ddc88190a969715658ebe7a8 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb840a5708190a9b64564b855fb22 completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf1efa3881909018b46a4b92b0a8 completed March 10, 2026, 5:41 a.m.
Created at: March 4, 2026, 7:37 p.m.