Triple

T6982035
Position Surface form Disambiguated ID Type / Status
Subject Mikołaj E161869 entity
Predicate equivalentNameInSpanish P28329 FINISHED
Object Nicolás E77168 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: Nicolás | Statement: [Mikołaj, equivalentNameInSpanish, Nicolás]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nicolás
Context triple: [Mikołaj, equivalentNameInSpanish, Nicolás]
  • A. Nicolás chosen
    Nicolás is a masculine given name of Greek origin, commonly used in Spanish-speaking countries and derived from the name Nicholas, meaning "victory of the people."
  • B. Felipe Fermín Paúl
    Felipe Fermín Paúl was a Venezuelan statesman and independence leader who helped shape the country’s break from Spanish colonial rule in the early 19th century.
  • C. Mariano
    Mariano is a masculine given name of Spanish and Portuguese origin, commonly used in various Spanish-speaking and Latin cultures.
  • D. Enrique
    Enrique is a Spanish given name equivalent to the English name Henry.
  • E. Juan Antonio
    Juan Antonio is a charismatic Spanish painter and romantic lead in the film "Vicky Cristina Barcelona," whose passionate relationships drive much of the movie’s drama.
  • 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_69c68855dc0481909b4c7e9e9ed273db completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6db6d3f3c8190b0121f7934440c34 completed March 27, 2026, 7:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a0e34288190ad2decbc18190c6b completed March 28, 2026, 5:41 a.m.
Created at: March 27, 2026, 2:31 p.m.