Triple

T14853713
Position Surface form Disambiguated ID Type / Status
Subject Observe and Report E349296 entity
Predicate productionCompany P490 FINISHED
Object De Line Pictures E250962 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: De Line Pictures | Statement: [Observe and Report, productionCompany, De Line Pictures]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: De Line Pictures
Context triple: [Observe and Report, productionCompany, De Line Pictures]
  • A. De Line Pictures chosen
    De Line Pictures is a film production company known for producing major Hollywood movies, including the 2003 remake of "The Italian Job."
  • B. BorderLine Films
    BorderLine Films is an independent film production company known for producing dark, character-driven dramas and psychological thrillers.
  • C. Tree Line Films
    Tree Line Films is a film production company known for working on major feature films, including the 2007 Western thriller "3:10 to Yuma."
  • D. Dendy Films
    Dendy Films is an Australian film distribution company known for releasing independent, arthouse, and international cinema.
  • E. Nyerai Films
    Nyerai Films is a Zimbabwean film production company known for creating socially conscious, women-centered stories under the leadership of writer and filmmaker Tsitsi Dangarembga.
  • 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_69d822ed7e1881909b90fca143ad7e34 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69ded44318f0819080b6c599f2d3474f completed April 14, 2026, 11:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe6506ace48190819504b93f575660 completed May 8, 2026, 10:34 p.m.
Created at: April 10, 2026, 1:54 a.m.