Triple

T19448997
Position Surface form Disambiguated ID Type / Status
Subject Santo Antônio Dam E486561 entity
Predicate locatedIn P40 FINISHED
Object Porto Velho NE NERFINISHED

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: Porto Velho | Statement: [Santo Antônio Dam, locatedIn, Porto Velho]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Porto Velho
Context triple: [Santo Antônio Dam, locatedIn, Porto Velho]
  • A. Porto Velho chosen
    Porto Velho is the capital and largest city of the Brazilian state of Rondônia, located in the western Amazon region.
  • B. Dourados
    Dourados is a major agricultural and commercial city in the Brazilian state of Mato Grosso do Sul, known as an important regional economic and educational center.
  • C. Manaus
    Manaus is a major Brazilian city and capital of the state of Amazonas, known as a key gateway to the Amazon rainforest and an important industrial and cultural center in the region.
  • D. Goiânia
    Goiânia is the capital and largest city of the Brazilian state of Goiás, known as a major regional center for agriculture, industry, and services in central Brazil.
  • E. Ariquemes
    Ariquemes is a municipality and important regional center in the Brazilian state of Rondônia, known for agriculture, cattle ranching, and its role in Amazonian frontier development.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8d7ad488190a3373045029b0f3b completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e6338be5a48190973d9ecae853900c completed April 20, 2026, 2:09 p.m.
Created at: April 10, 2026, 1:38 p.m.