Triple

T18056322
Position Surface form Disambiguated ID Type / Status
Subject Mr. Nice E432047 entity
Predicate castMember P1668 FINISHED
Object Elsa Pataky NE NERFINISHED

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: Elsa Pataky | Statement: [Mr. Nice, castMember, Elsa Pataky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elsa Pataky
Context triple: [Mr. Nice, castMember, Elsa Pataky]
  • A. Elsa Pataky chosen
    Elsa Pataky is a Spanish actress and model best known for her roles in the Fast & Furious film franchise and various international action and thriller movies.
  • B. Elsa Martinelli
    Elsa Martinelli was an Italian actress and fashion model known for her international film career in the 1950s and 1960s.
  • C. Elena Anaya
    Elena Anaya is a Spanish actress known for her roles in both European cinema and Hollywood productions, including prominent performances in films like "The Skin I Live In."
  • D. Chimene Diaz
    Chimene Diaz is the older sister of American actress Cameron Diaz and is a non-celebrity who has largely stayed out of the public spotlight.
  • E. Bérénice Marlohe
    Bérénice Marlohe is a French actress best known internationally for her role as Sévérine in the James Bond film "Skyfall."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b906482481908183315b9ecf9994 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4c102d08081908d2419c898213400 completed April 19, 2026, 11:48 a.m.
Created at: April 10, 2026, 10:26 a.m.