Triple

T12065423
Position Surface form Disambiguated ID Type / Status
Subject Judy E287281 entity
Predicate productionCompany P490 FINISHED
Object Calamity Films E864468 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: Calamity Films | Statement: [Judy, productionCompany, Calamity Films]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Calamity Films
Context triple: [Judy, productionCompany, Calamity Films]
  • A. Calamity Films chosen
    Calamity Films is a British film and television production company known for producing contemporary, often character-driven features such as the romantic comedy "Last Christmas."
  • B. Ombra Films
    Ombra Films is a film production company known for working on action and thriller movies, including the crime thriller "Run All Night."
  • C. Cinelou Films
    Cinelou Films is an independent American film production company known for producing character-driven dramas such as the 2014 film "Cake."
  • D. Valoria Films
    Valoria Films is a film distribution company known for handling the release of various international and independent movies.
  • E. Aquarius Films
    Aquarius Films is an Australian film and television production company known for creating distinctive, character-driven screen content for both local and international audiences.
  • 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_69d6ab4846e081908ee7bbd66a6d3459 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d90440dd988190ae2b80367aceb6f7 completed April 10, 2026, 2:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69f5f656f4f481909a1ecac3f89da374 completed May 2, 2026, 1:04 p.m.
Created at: April 8, 2026, 9:48 p.m.