Triple

T696031
Position Surface form Disambiguated ID Type / Status
Subject Lorne Balfe E13895 entity
Predicate name P16 FINISHED
Object Lorne Balfe E13895 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: Lorne Balfe | Statement: [Lorne Balfe, name, Lorne Balfe]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lorne Balfe
Context triple: [Lorne Balfe, name, Lorne Balfe]
  • A. Lorne Balfe chosen
    Lorne Balfe is a Scottish composer and producer known for his work on major film, television, and video game scores, often in the action and blockbuster genres.
  • B. Daniel Pemberton
    Daniel Pemberton is a British composer known for his innovative and eclectic film scores across major Hollywood and independent productions.
  • C. Patrick Doyle
    Patrick Doyle is a Scottish film composer renowned for his scores for major movies including romantic comedies like "Bridget Jones’s Diary" and numerous Shakespeare adaptations.
  • D. Alexandre Desplat
    Alexandre Desplat is an acclaimed French film composer known for his elegant, emotionally nuanced scores for movies such as The King’s Speech, The Grand Budapest Hotel, and The Shape of Water.
  • E. Rupert Gregson-Williams
    Rupert Gregson-Williams is a British film and television composer known for scoring major Hollywood productions such as "Wonder Woman," "Aquaman," and "The Crown."
  • 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_69a493406c408190957eeec9048a8fb6 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a0c5f51c8190acc4915099e4b384 completed March 1, 2026, 8:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69a65e376f748190af7088c53605c0cb completed March 3, 2026, 4:06 a.m.
Created at: March 1, 2026, 7:36 p.m.