Triple

T1688473
Position Surface form Disambiguated ID Type / Status
Subject Mosquera E36495 entity
Predicate borderedBy P224 FINISHED
Object Sibaté E45543 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: Sibaté | Statement: [Mosquera, borderedBy, Sibaté]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sibaté
Context triple: [Mosquera, borderedBy, Sibaté]
  • A. Sibaté chosen
    Sibaté is a municipality in central Colombia known for its agricultural production and proximity to Bogotá within the Cundinamarca Department.
  • B. Gurabo
    Gurabo is a municipality in eastern Puerto Rico known for its suburban character, scenic hills, and integration into the greater San Juan metropolitan region.
  • C. Bejucal
    Bejucal is a Cuban town and municipality known for its historic role in the island’s early railway system and its traditional “Charangas de Bejucal” carnival festivities.
  • D. Comayagüela
    Comayagüela is a major urban district of Honduras that, together with Tegucigalpa, forms the country’s capital area.
  • E. Tocancipá
    Tocancipá is a Colombian municipality in the department of Cundinamarca, known for its industrial activity, motorsport circuit, and proximity to Bogotá.
  • 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_69a886151508819084fa7f1ce6e05577 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa6296655c8190835ec0d20f7460ca completed March 6, 2026, 5:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69adeac637748190b67ddd9eedc3698d completed March 8, 2026, 9:31 p.m.
Created at: March 4, 2026, 7:29 p.m.