Triple

T22401145
Position Surface form Disambiguated ID Type / Status
Subject Sachaca District E553761 entity
Predicate administrativeCenter P1474 FINISHED
Object Sachaca 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: Sachaca | Statement: [Sachaca District, administrativeCenter, Sachaca]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sachaca
Context triple: [Sachaca District, administrativeCenter, Sachaca]
  • A. Sachaca chosen
    Sachaca is a town in southern Peru that serves as the administrative and urban center of the Sachaca District in the Arequipa region.
  • B. Rurrenabaque
    Rurrenabaque is a small Bolivian town known as a popular gateway to the Amazon rainforest and nearby Madidi National Park.
  • C. Capiatá
    Capiatá is a city in Paraguay known as part of the Greater Asunción metropolitan area and an important urban and commercial center in the Central Department.
  • D. Latacunga
    Latacunga is a highland city in central Ecuador, known as the capital of Cotopaxi Province and a gateway to the Cotopaxi volcano.
  • E. Tocaima
    Tocaima is a historic Colombian town in the Cundinamarca Department, known for its warm climate and thermal springs.
  • 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_69e11e4da7048190b4387d422a9a0de5 completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f158b39c908190a735aa860d733869 completed April 29, 2026, 1:02 a.m.
Created at: April 16, 2026, 8:46 p.m.