Triple

T9919917
Position Surface form Disambiguated ID Type / Status
Subject Graeme Revell E185967 entity
Predicate name P16 FINISHED
Object Graeme Revell E185967 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: Graeme Revell | Statement: [Graeme Revell, name, Graeme Revell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Graeme Revell
Context triple: [Graeme Revell, name, Graeme Revell]
  • A. Graeme Revell chosen
    Graeme Revell is a New Zealand-born composer best known for his atmospheric film scores across genres including horror, action, and science fiction.
  • B. Andrew Rennison
    Andrew Rennison is a British public official known for serving as the inaugural Surveillance Camera Commissioner, overseeing the regulation and ethical use of CCTV and related surveillance technologies in the UK.
  • C. Jonathan Gledhill
    Jonathan Gledhill was an English Anglican bishop who served in senior episcopal roles in the Church of England, including as Bishop of Stafford.
  • D. Graeme Gibson
    Graeme Gibson was a Canadian novelist, environmentalist, and cultural advocate known for his contributions to Canadian literature and his long partnership with writer Margaret Atwood.
  • E. Graham Walters
    Graham Walters is a film producer best known for his work on the acclaimed Pixar animated feature "Finding Nemo."
  • 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_69ca829b45f481909040f7b99a1976ed completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cdb5699bc48190961e036d1131fef0 completed April 2, 2026, 12:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69d3000b21088190aa3ebb2ccbce9a6e completed April 6, 2026, 12:36 a.m.
Created at: March 30, 2026, 8:42 p.m.