Triple

T13616922
Position Surface form Disambiguated ID Type / Status
Subject The Witches of Eastwick E325338 entity
Predicate composer P1361 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: [The Witches of Eastwick, composer, John Williams]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: John Williams
Context triple: [The Witches of Eastwick, composer, 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_69d8076aae28819092cf636190ee5529 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb0ad0a7c81909c7972187202db96 completed April 12, 2026, 2:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f78ae7d6908190836172e955b0d7e8 completed May 3, 2026, 5:50 p.m.
Created at: April 9, 2026, 9:50 p.m.