Triple

T13710132
Position Surface form Disambiguated ID Type / Status
Subject Ana Navarro E328748 entity
Predicate birthName P65 FINISHED
Object Ana Violeta Navarro-Cárdenas E328748 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: Ana Violeta Navarro-Cárdenas | Statement: [Ana Navarro, birthName, Ana Violeta Navarro-Cárdenas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ana Violeta Navarro-Cárdenas
Context triple: [Ana Navarro, birthName, Ana Violeta Navarro-Cárdenas]
  • A. Ana Valenzuela
    Ana Valenzuela is a notable individual distinguished enough within her field or public life to be recognized as a prominent bearer of the Valenzuela surname.
  • B. Ana Navarro chosen
    Ana Navarro is a Nicaraguan-American Republican strategist, political commentator, and television personality known for her outspoken views on U.S. politics.
  • C. Silvia Navarro
    Silvia Navarro is a Mexican actress best known for her leading roles in popular telenovelas and television dramas.
  • D. Luna Encinas Cruz
    Luna Encinas Cruz is the daughter of Spanish actors Javier Bardem and Penélope Cruz.
  • E. Paola Núñez
    Paola Núñez is a Mexican actress and producer known for her work in telenovelas and English-language television and film, including prominent roles in series like The Purge and the film Bad Boys for Life.
  • 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_69d80770b9bc81909f70c8c317d53cff completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dd43949e6c8190ae5e4fa119cde33a completed April 13, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69fde15abe6c8190a6212861bbce790e completed May 8, 2026, 1:12 p.m.
Created at: April 9, 2026, 9:54 p.m.