Triple

T10225479
Position Surface form Disambiguated ID Type / Status
Subject Stealing Beauty E243192 entity
Predicate stars P1956 FINISHED
Object Rachel Weisz E30057 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: Rachel Weisz | Statement: [Stealing Beauty, stars, Rachel Weisz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rachel Weisz
Context triple: [Stealing Beauty, stars, Rachel Weisz]
  • A. Rachel Weisz chosen
    Rachel Weisz is an Academy Award–winning British actress known for her versatile performances in films such as "The Constant Gardener," "The Mummy," and "The Favourite."
  • B. Eva Green
    Eva Green is a French actress known for her dark, intense performances in film and television, including prominent roles in projects like "Casino Royale" and "Penny Dreadful."
  • C. Sophie Fiennes
    Sophie Fiennes is a British film director and producer known for her innovative documentaries and collaborations with artists and philosophers.
  • D. Rebecca Hall
    Rebecca Hall is a British-American actress and filmmaker known for her nuanced performances in films such as "Vicky Cristina Barcelona," "The Town," and "Christine."
  • E. Isabelle Carré
    Isabelle Carré is a French actress known for her performances in films such as "Se souvenir des belles choses," for which she won the César Award for Best Actress.
  • 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_69d381b0f97c819085c9b45799a5fb7c completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4d1f9cf6c81909a6b9e9b9d0a79fe completed April 7, 2026, 9:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69d79490a3c48190a58bff2f63e5873d completed April 9, 2026, 11:59 a.m.
Created at: April 6, 2026, 11:17 a.m.