Triple

T11694339
Position Surface form Disambiguated ID Type / Status
Subject Chicago (1927 film) E277952 entity
Predicate distributor P1951 FINISHED
Object Pathé Exchange E709716 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 Exchange | Statement: [Chicago (1927 film), distributor, Pathé Exchange]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pathé Exchange
Context triple: [Chicago (1927 film), distributor, Pathé Exchange]
  • A. Pathé Exchange chosen
    Pathé Exchange was an early 20th-century American film distribution company known for handling and releasing numerous silent and early sound films.
  • B. Pathé
    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. 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. Cineplex Cinemas
    Cineplex Cinemas is a major Canadian movie theatre chain offering multiplex cinema experiences with multiple screens, concessions, and modern film presentation technologies.
  • E. Wanda Cinemas
    Wanda Cinemas is a major Chinese cinema chain known for operating a large network of modern movie theaters across China.
  • 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_69d6aafe02d881909900d54ad7d4af84 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a47b9eb48190976a35e91e25b56b completed April 10, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69ef1461b2f0819091ef2a0627ffe5f5 completed April 27, 2026, 7:46 a.m.
Created at: April 8, 2026, 9:40 p.m.