Triple

T90272
Position Surface form Disambiguated ID Type / Status
Subject South America E1813 entity
Predicate hasMajorCity P316 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: [South America, hasMajorCity, Bogotá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bogotá
Context triple: [South America, hasMajorCity, 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. 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.
  • C. Guayaquil
    Guayaquil is a major Pacific port city in southwestern Ecuador and the country’s principal commercial and industrial center.
  • D. Santiago
    Santiago is the capital and primary economic, political, and cultural center of Chile, located in the country’s central valley.
  • E. Santiago de Veraguas
    Santiago de Veraguas is a principal urban and commercial center in western Panama and the capital of Veraguas Province.
  • 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_69a24d1a97dc819094e6c021fe9b05a7 completed Feb. 28, 2026, 2:04 a.m.
NER Named-entity recognition batch_69a24f6c29888190890caa7872d63ac6 completed Feb. 28, 2026, 2:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2c936a0b48190950c2684055ff591 completed Feb. 28, 2026, 10:53 a.m.
Created at: Feb. 28, 2026, 2:07 a.m.