Triple

T3016474
Position Surface form Disambiguated ID Type / Status
Subject Enola Holmes E82347 entity
Predicate musicBy P1952 FINISHED
Object Daniel Pemberton E79203 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: Daniel Pemberton | Statement: [Enola Holmes, musicBy, Daniel Pemberton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daniel Pemberton
Context triple: [Enola Holmes, musicBy, Daniel Pemberton]
  • A. Daniel Pemberton chosen
    Daniel Pemberton is a British composer known for his innovative and eclectic film scores across major Hollywood and independent productions.
  • B. Lorne Balfe
    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.
  • C. 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.
  • D. Michael Kamen
    Michael Kamen was an American composer and conductor renowned for his film and television scores, including major works in action cinema and acclaimed historical dramas.
  • 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_69ad8b1eb53481908c39bbcd1ec104b2 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad9a6c56708190b7d8d08bca727cc1 completed March 8, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b12e6eac1481909d56844e53c37b59 completed March 11, 2026, 8:57 a.m.
Created at: March 8, 2026, 3 p.m.