Triple

T18957365
Position Surface form Disambiguated ID Type / Status
Subject Nina Ivanovna Yakushova E463815 entity
Predicate givenName P17 FINISHED
Object Nina 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: Nina | Statement: [Nina Ivanovna Yakushova, givenName, Nina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nina
Context triple: [Nina Ivanovna Yakushova, givenName, Nina]
  • A. Nina
    Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
  • B. Nina chosen
    Nina is a feminine given name used in various cultures, often as a short form of names like Antonina or Giannina, and borne by numerous notable figures in the arts and public life.
  • C. Nina
    Nina is a central character in the British cult film "Human Traffic," which explores the lives and clubbing culture of young people in Cardiff.
  • D. Nina
    Nina is a biographical drama film written and directed by Cynthia Mort that portrays the life and struggles of legendary musician Nina Simone.
  • E. Nita
    Nita is a feminine given name commonly used as a shortened or affectionate form of longer names such as Juanita.
  • 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_69d8dcffc278819086792a4ebfddfafa completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5d5cee8348190b6506b10aed6c58a completed April 20, 2026, 7:29 a.m.
Created at: April 10, 2026, noon