Triple

T512751
Position Surface form Disambiguated ID Type / Status
Subject Diocese of Colombia E10641 entity
Predicate seeCity P3207 FINISHED
Object Bogotá E1526 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: Bogotá | Statement: [Diocese of Colombia, seeCity, Bogotá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bogotá
Context triple: [Diocese of Colombia, seeCity, Bogotá]
  • A. Bogotá chosen
    Bogotá is the high-altitude capital and largest city of Colombia, known as a major political, economic, and cultural center in South America.
  • B. Cali
    Cali is a major city in southwestern Colombia known as an important economic center and the country’s capital of salsa.
  • C. Caracas
    Caracas is the capital and largest city of Venezuela, known as a major political, cultural, and economic center in northern South America.
  • D. Quito
    Quito is the high-altitude Andean city that serves as Ecuador’s political and cultural center, renowned for its well-preserved colonial historic center and dramatic mountain setting.
  • E. Anapoima
    Anapoima is a warm-climate resort town and popular weekend getaway located in the Cundinamarca department of central Colombia.
  • 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_69a2e84a0d08819087e01863fcd9abf1 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f1804e908190a1d34ac952e84a3f completed Feb. 28, 2026, 1:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4c66b91608190aff4623917cf3ae2 completed March 1, 2026, 11:06 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.