Triple

T5643182
Position Surface form Disambiguated ID Type / Status
Subject Manuel Azaña E124317 entity
Predicate familyName P18 FINISHED
Object Azaña E124317 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: Azaña | Statement: [Manuel Azaña, familyName, Azaña]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Azaña
Context triple: [Manuel Azaña, familyName, Azaña]
  • A. Azaña chosen
    Azaña is the surname of Manuel Azaña, a prominent Spanish politician and writer who served as President of the Second Spanish Republic.
  • B. Norzagaray
    Norzagaray is a landlocked municipality in the province of Bulacan in the Philippines, known for its quarrying industry and natural attractions such as dams, rivers, and limestone formations.
  • C. Aranzazu
    Aranzazu is a small Colombian town located in the mountainous coffee-growing region of the Caldas Department.
  • D. Negrín
    Negrín is a Spanish surname most notably associated with Juan Negrín, the physician and politician who served as Prime Minister of the Second Spanish Republic during the Spanish Civil War.
  • E. Molinero
    Molinero is a Spanish surname that corresponds to the German surname Müller, both historically referring to the occupation of a miller.
  • 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_69c00824643c81909ffdb888a2d35189 completed March 22, 2026, 3:17 p.m.
NER Named-entity recognition batch_69c022a7cd9c819087f86f60e8b65fc3 completed March 22, 2026, 5:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69c04d80396c81909e26da3b5ca5ea34 completed March 22, 2026, 8:13 p.m.
Created at: March 22, 2026, 3:41 p.m.