Triple

T15440753
Position Surface form Disambiguated ID Type / Status
Subject Dracula (1979 film) E369892 entity
Predicate castMember P1668 FINISHED
Object Kate Nelligan E607851 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: Kate Nelligan | Statement: [Dracula (1979 film), castMember, Kate Nelligan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kate Nelligan
Context triple: [Dracula (1979 film), castMember, Kate Nelligan]
  • A. Kate Nelligan chosen
    Kate Nelligan is a Canadian actress acclaimed for her work in film, television, and theatre, noted for her intense dramatic performances and multiple award nominations.
  • B. Bridget Tierney
    Bridget Tierney is an actress known for her role in the television film "In the Gloaming."
  • C. Kate Hennessy
    Kate Hennessy is an American writer and the granddaughter of Catholic social activist Dorothy Day, known for her memoirs and work chronicling her family’s legacy.
  • D. Kate Mullen
    Kate Mullen is the central protagonist of the work "Ransom," around whom the main narrative and its conflicts revolve.
  • E. Christine Brennan
    Christine Brennan is an American sports journalist, author, and television commentator known for her coverage of the Olympics and advocacy for women in sports.
  • 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_69d85a19180081909925012fbf4e62a3 completed April 10, 2026, 2:02 a.m.
NER Named-entity recognition batch_69e03eddf258819082679970b7d2b6af completed April 16, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0167369d9481909015c34d475fac14 completed May 11, 2026, 5:20 a.m.
Created at: April 10, 2026, 3:21 a.m.