Triple

T5095277
Position Surface form Disambiguated ID Type / Status
Subject Pathé E114849 entity
Predicate hasSubsidiary P254 FINISHED
Object Pathé Cinémas E114849 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: Pathé Cinémas | Statement: [Pathé, hasSubsidiary, Pathé Cinémas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pathé Cinémas
Context triple: [Pathé, hasSubsidiary, Pathé Cinémas]
  • A. Gaumont cinemas
    Gaumont cinemas is a historic French cinema chain known for operating movie theaters across France and being one of the oldest names in the film exhibition industry.
  • B. Pathé chosen
    Pathé is a historic French film production and distribution company that also operated as a major record label in the early and mid-20th century.
  • C. Odeon Cinemas
    Odeon Cinemas is a major British and European cinema chain known for operating numerous multiplex movie theaters across the UK and beyond.
  • D. Regal Cinemas
    Regal Cinemas is a major American movie theater chain known for operating multiplex cinemas across the United States.
  • E. Marcus Theatres
    Marcus Theatres is a major American movie theater chain known for operating multiplex cinemas across the Midwest and other regions of the United States.
  • 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_69bd443fc49c819089629c00e311310c completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7563ad608190879a26a0bf07c3f6 completed March 20, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69beba7b87c08190a2581c87f965fa9f completed March 21, 2026, 3:34 p.m.
Created at: March 20, 2026, 1:40 p.m.