Triple

T10468614
Position Surface form Disambiguated ID Type / Status
Subject Bride and Prejudice E246867 entity
Predicate productionCompany P490 FINISHED
Object Pathé 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 | Statement: [Bride and Prejudice, productionCompany, Pathé]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pathé
Context triple: [Bride and Prejudice, productionCompany, Pathé]
  • A. 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.
  • B. Gaumont
    Gaumont is a historic French film and television production company, recognized as one of the oldest continuously operating studios in the world.
  • C. 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.
  • D. Pathé Exchange
    Pathé Exchange was an early 20th-century American film distribution company known for handling and releasing numerous silent and early sound films.
  • E. CinéArts
    CinéArts is a Cinemark-owned brand of upscale movie theaters that focuses on presenting independent, foreign, and art-house films in a premium cinema environment.
  • 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_69d381c16c248190a2fe5b471e584e9c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d5092ef810819093a4d1df83aeac09 completed April 7, 2026, 1:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69d89ff1cd948190a1ef331fb810bf26 completed April 10, 2026, 7 a.m.
Created at: April 6, 2026, 12:20 p.m.