Triple

T2270718
Position Surface form Disambiguated ID Type / Status
Subject Cullen Landis E50649 entity
Predicate employer P7 FINISHED
Object Pathé Exchange 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 Exchange | Statement: [Cullen Landis, employer, Pathé Exchange]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Pathé Exchange
Context triple: [Cullen Landis, employer, Pathé Exchange]
  • 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 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.
  • C. Wanda Cinemas
    Wanda Cinemas is a major Chinese cinema chain known for operating a large network of modern movie theaters across China.
  • D. Regal Cinemas
    Regal Cinemas is a major American movie theater chain known for operating multiplex cinemas across the United States.
  • E. Hoyts
    Hoyts is a major Australian cinema chain and film distribution company known for operating numerous movie theatres across Australia and New Zealand.
  • 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_69a88b05910c8190a9a2b1ff230c85f9 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc1be90708190b8878c393dd2a42d completed March 7, 2026, 6:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71d97a108190a26ffd20fac91a7e completed March 9, 2026, 7:08 a.m.
Created at: March 4, 2026, 7:48 p.m.