Triple

T1822788
Position Surface form Disambiguated ID Type / Status
Subject La Calera E40577 entity
Predicate borders P224 FINISHED
Object Sopó E40022 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: Sopó | Statement: [La Calera, borders, Sopó]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sopó
Context triple: [La Calera, borders, Sopó]
  • A. Sopó chosen
    Sopó is a small municipality in the department of Cundinamarca, Colombia, known for its scenic Andean landscapes and dairy production.
  • B. Comayagüela
    Comayagüela is a major urban district of Honduras that, together with Tegucigalpa, forms the country’s capital area.
  • 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. Sibaté
    Sibaté is a municipality in central Colombia known for its agricultural production and proximity to Bogotá within the Cundinamarca Department.
  • E. Samborondón
    Samborondón is a rapidly growing, affluent canton and town in coastal Ecuador, located across the river from Guayaquil and known for its upscale residential and commercial developments.
  • 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_69a8864526c081908a3a4d74f689e2c5 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa662d13e88190a22b0faf0d848c7d completed March 6, 2026, 5:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69adc9af6d3c8190a9047d06f38f210d completed March 8, 2026, 7:10 p.m.
Created at: March 4, 2026, 7:32 p.m.