Triple

T3535869
Position Surface form Disambiguated ID Type / Status
Subject Mato Grosso E74770 entity
Predicate capital P234 FINISHED
Object Cuiabá E144203 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: Cuiabá | Statement: [Mato Grosso, capital, Cuiabá]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cuiabá
Context triple: [Mato Grosso, capital, Cuiabá]
  • A. Cuiabá chosen
    Cuiabá is the capital city of Brazil’s Mato Grosso state and a primary urban hub and access point for exploring the Pantanal wetlands.
  • B. Lajeado
    Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
  • C. Magé
    Magé is a municipality in the state of Rio de Janeiro, Brazil, located in the metropolitan region of Rio de Janeiro and known for its coastal setting and historical significance.
  • D. Corumbá
    Corumbá is a Brazilian city in the state of Mato Grosso do Sul, known as a key gateway to the Pantanal wetlands and an important regional center for river trade and ecotourism.
  • E. Caxangá
    Caxangá is a neighborhood and important urban area within the city of Recife, Brazil.
  • 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_69ad85d1a3948190931fd1ea1f49717b completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbcc4c1d081908938efb71938e1a3 completed March 8, 2026, 6:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69b38bd237e881909df210a42346b572 completed March 13, 2026, 4 a.m.
Created at: March 8, 2026, 3:20 p.m.