Triple

T5342402
Position Surface form Disambiguated ID Type / Status
Subject Labyrinth E123973 entity
Predicate editedBy P1954 FINISHED
Object John Grover E489022 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 Grover | Statement: [Labyrinth, editedBy, John Grover]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Grover
Context triple: [Labyrinth, editedBy, John Grover]
  • A. John Grover chosen
    John Grover is a film editor best known for his work on action and thriller movies, including the James Bond film series.
  • B. John Gibbon
    John Gibbon was a 19th-century United States Army officer and Civil War general who later played a key role in the Indian Wars, including campaigns against the Nez Perce.
  • C. John Bishop
    John Bishop is an English stand-up comedian, actor, and television presenter known for his energetic storytelling style and appearances on British panel shows and dramas.
  • D. Martin Mull
    Martin Mull is an American actor, comedian, and musician known for his dry wit and roles in television comedies and films.
  • E. Graham Crowden
    Graham Crowden was a British character actor known for his eccentric, often darkly comic roles in film and television, including notable performances in works like "If....", "A Very Peculiar Practice", and "Waiting for God."
  • 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_69bd464b07f8819095aa76577c9829e4 completed March 20, 2026, 1:06 p.m.
NER Named-entity recognition batch_69bd85cc5a9881909e23bf9c5b697a8e completed March 20, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf18cc387c8190a9fe430fe5bb38ce completed March 21, 2026, 10:16 p.m.
Created at: March 20, 2026, 2:01 p.m.