Triple

T15440742
Position Surface form Disambiguated ID Type / Status
Subject Dracula (1979 film) E369892 entity
Predicate musicComposer P32102 FINISHED
Object John Williams E20414 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 Williams | Statement: [Dracula (1979 film), musicComposer, John Williams]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Williams
Context triple: [Dracula (1979 film), musicComposer, John Williams]
  • A. John Williams
    John Williams was a colonial New England Puritan minister best known for his captivity narrative recounting his abduction during the 1704 Deerfield raid.
  • B. John Williams
    John Williams was a British actor known for his character roles in mid-20th-century films and television, including classic courtroom dramas and comedies.
  • C. John Williams chosen
    John Williams is an acclaimed American composer and conductor best known for his iconic film scores for franchises such as Star Wars, Indiana Jones, Harry Potter, and many others.
  • D. John Williams
    John Williams was a 19th-century British missionary renowned for his extensive evangelizing and church-building work in the South Pacific, particularly in Polynesia.
  • E. 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.
  • 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_69d85a19180081909925012fbf4e62a3 completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03eddf258819082679970b7d2b6af completed April 16, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff21a9b9188190a3f5de3ee18c5d3e completed May 9, 2026, 11:59 a.m.
Created at: April 10, 2026, 3:21 a.m.