Triple

T11994904
Position Surface form Disambiguated ID Type / Status
Subject The Disorderly Orderly E285503 entity
Predicate editor P1954 FINISHED
Object John Woodcock E958712 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 Woodcock | Statement: [The Disorderly Orderly, editor, John Woodcock]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Woodcock
Context triple: [The Disorderly Orderly, editor, John Woodcock]
  • A. John Woodcock chosen
    John Woodcock is a film editor best known for his work on movies such as "The Nutty Professor."
  • B. Jervis Johnson
    Jervis Johnson is a prominent British game designer best known for creating and developing tabletop wargames and board games for Games Workshop, including key contributions to Warhammer and related systems.
  • C. Edward Linden
    Edward Linden was an American cinematographer best known for his work on classic early Hollywood films, including the pioneering visual effects and photography of the 1933 monster movie "King Kong."
  • D. Ralph Brownrigg
    Ralph Brownrigg was a 17th-century English clergyman and academic who served as Bishop of Exeter in the Church of England.
  • E. Arthur Pierson
    Arthur Pierson was an American actor and later film and television director active in the early to mid-20th century.
  • 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903b211688190bfe6dd15c3f96d2f completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f49d183ee881908f1c0ca4562344a9 completed May 1, 2026, 12:31 p.m.
Created at: April 8, 2026, 9:46 p.m.