Triple

T14514240
Position Surface form Disambiguated ID Type / Status
Subject El ángel exterminador E340475 entity
Predicate castMember P1668 FINISHED
Object Rosa Elena Durgel E789827 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: Rosa Elena Durgel | Statement: [El ángel exterminador, castMember, Rosa Elena Durgel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rosa Elena Durgel
Context triple: [El ángel exterminador, castMember, Rosa Elena Durgel]
  • A. Rosa Elena Durgel chosen
    Rosa Elena Durgel was a Mexican actress known for her role in Luis Buñuel’s acclaimed surrealist film "The Exterminating Angel."
  • B. Margarita de Luria
    Margarita de Luria was the wife of the renowned 13th-century Aragonese admiral Roger of Lauria.
  • C. Rufina Gurevich
    Rufina Gurevich is a mathematician known for her work in areas influenced by Lev Pontryagin’s contributions to topology and control theory.
  • D. María Corda
    María Corda was a prominent Hungarian silent film actress of the 1920s, known for her glamorous screen presence and leading roles in European and early Hollywood cinema.
  • E. Ludmila Bragina
    Ludmila Bragina is a former Soviet middle-distance runner best known for winning Olympic gold and setting multiple world records in the 1500 metres in the early 1970s.
  • 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_69d822d9c0408190b9a2b3643e58bb4d completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69de9a6d82988190b6f957012bcc63d4 completed April 14, 2026, 7:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe0cd6c0dc8190847a887ae51c6c5b completed May 8, 2026, 4:18 p.m.
Created at: April 10, 2026, 1:21 a.m.