Triple

T8914105
Position Surface form Disambiguated ID Type / Status
Subject Cyanerpes nitidus E212253 entity
Predicate nativeTo P410 FINISHED
Object Colombia E12035 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: Colombia | Statement: [Cyanerpes nitidus, nativeTo, Colombia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Colombia
Context triple: [Cyanerpes nitidus, nativeTo, Colombia]
  • A. Colombia
    Colombia is a station on Madrid's Metro network, serving Line 8 and acting as an important interchange point in the city's public transportation system.
  • B. Colombia chosen
    Colombia is a transcontinental country in northern South America, known for its diverse landscapes from Andes mountains to Amazon rainforest, rich cultural heritage, and major cities like Bogotá and Medellín.
  • C. Chinchiná
    Chinchiná is a Colombian town and municipality known for its coffee production and location in the central Andean region.
  • D. Ecuador
    Ecuador is a South American country on the Pacific coast, known for its diverse geography that includes part of the Amazon rainforest, the Andean highlands, and the Galápagos Islands.
  • E. Venezuela
    Venezuela is a South American country known for its vast oil reserves, diverse landscapes ranging from Caribbean coastlines to Andean mountains and Amazon rainforest, and its Spanish-speaking population.
  • 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_69ca8393b1808190bd4336787ffa2c40 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc65276b788190ab76372d0d871fef completed April 1, 2026, 12:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfba4102e881908441669e14e5c491 completed April 3, 2026, 1:01 p.m.
Created at: March 30, 2026, 6:56 p.m.