Triple

T19480088
Position Surface form Disambiguated ID Type / Status
Subject General Roca Department E487356 entity
Predicate hasTown P847 FINISHED
Object Fernández Oro 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: Fernández Oro | Statement: [General Roca Department, hasTown, Fernández Oro]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Fernández Oro
Context triple: [General Roca Department, hasTown, Fernández Oro]
  • A. Fernández Oro chosen
    Fernández Oro is a town in Argentina known for its fruit production and location within the Alto Valle region of Río Negro Province.
  • B. Carlos Ibáñez del Campo
    Carlos Ibáñez del Campo was a Chilean military officer and politician who twice served as President of Chile, playing a major role in the country’s early 20th-century political history.
  • C. José Luzán
    José Luzán was an 18th-century Spanish painter and influential teacher best known for mentoring the young Francisco Goya.
  • D. Osvaldo Dorticós Torrado
    Osvaldo Dorticós Torrado was a Cuban politician and lawyer who served as the President of Cuba from 1959 to 1976 during the early decades of the Cuban Revolution.
  • E. Antonio López Habas
    Antonio López Habas is a Spanish football manager best known for leading Atlético de Kolkata to success in the Indian Super League.
  • 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_69d8e8d924388190b847cb15bb3d0aff completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e6343882a88190b3cfa65e6cac80d3 completed April 20, 2026, 2:12 p.m.
Created at: April 10, 2026, 1:39 p.m.