Triple

T10553787
Position Surface form Disambiguated ID Type / Status
Subject Beauty Shop E249021 entity
Predicate cinematographyBy P1953 FINISHED
Object Peter Lyons Collister
Peter Lyons Collister is an American cinematographer known for his work on numerous feature films and television projects.
E237367 NE FINISHED

How this triple was built (4 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: Peter Lyons Collister | Statement: [Beauty Shop, cinematographyBy, Peter Lyons Collister]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peter Lyons Collister
Context triple: [Beauty Shop, cinematographyBy, Peter Lyons Collister]
  • A. Peter Collinson
    Peter Collinson was a British film director best known for his work on stylish 1960s and 1970s crime and thriller films.
  • B. Robert James-Collier
    Robert James-Collier is an English actor best known for his roles in the television series "Downton Abbey" and "Coronation Street."
  • C. James Collinson
    James Collinson was a 19th-century English painter associated with the Pre-Raphaelite Brotherhood and early Victorian art circles.
  • D. John Lyons
    John Lyons is a film producer best known for his work on major Hollywood comedies, including the Austin Powers series.
  • E. Peter LeFanu Lumsdaine
    Peter LeFanu Lumsdaine is a mathematician known for his work in category theory, type theory, and homotopy type theory.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Peter Lyons Collister
Triple: [Beauty Shop, cinematographyBy, Peter Lyons Collister]
Generated description
Peter Lyons Collister is an American cinematographer known for his work on numerous feature films and television projects.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Peter Lyons Collister
Target entity description: Peter Lyons Collister is an American cinematographer known for his work on numerous feature films and television projects.
  • A. Peter Lyons Collister chosen
    Peter Lyons Collister is an American cinematographer known for his work on numerous feature films, including the action-comedy "Barely Lethal."
  • B. Peter Collinson
    Peter Collinson was a British film director best known for his work on stylish 1960s and 1970s crime and thriller films.
  • C. Robert James-Collier
    Robert James-Collier is an English actor best known for his roles in the television series "Downton Abbey" and "Coronation Street."
  • D. James Collinson
    James Collinson was a 19th-century English painter associated with the Pre-Raphaelite Brotherhood and early Victorian art circles.
  • E. John Lyons
    John Lyons is a film producer best known for his work on major Hollywood comedies, including the Austin Powers series.
  • F. None of above.

Provenance (5 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d527118da081909ca61bc555a17609 completed April 7, 2026, 3:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69d9346f6a38819087647e7a09f40c41 completed April 10, 2026, 5:33 p.m.
NEDg Description generation batch_69d938c8b25c8190bb048053d8668e5c completed April 10, 2026, 5:52 p.m.
NED2 Entity disambiguation (via description) batch_69d939b1844881908c8fbcb9488863f6 completed April 10, 2026, 5:56 p.m.
Created at: April 6, 2026, 12:34 p.m.