Triple

T15636739
Position Surface form Disambiguated ID Type / Status
Subject Christine (2016 film) E375963 entity
Predicate productionCompany P490 FINISHED
Object BorderLine Films E417698 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: BorderLine Films | Statement: [Christine (2016 film), productionCompany, BorderLine Films]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: BorderLine Films
Context triple: [Christine (2016 film), productionCompany, BorderLine Films]
  • A. BorderLine Films chosen
    BorderLine Films is an independent film production company known for producing dark, character-driven dramas and psychological thrillers.
  • B. 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."
  • C. Crossroads Films
    Crossroads Films is a film and commercial production company known for producing independent features and high-end advertising content.
  • D. Cinelou Films
    Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
  • E. Beyond Films
    Beyond Films is an Australian film distribution and production company known for handling a range of independent and international titles.
  • 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_69d85cd035a48190b73d5579ab73969a completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04eba51f08190ac5d9de7fc89405a completed April 16, 2026, 2:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff5f4923ac8190a03fe1f2c878c27e completed May 9, 2026, 4:22 p.m.
Created at: April 10, 2026, 4:14 a.m.