Triple

T20356997
Position Surface form Disambiguated ID Type / Status
Subject Munduruku people E496675 entity
Predicate primaryState P3447 FINISHED
Object Mato Grosso 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: Mato Grosso | Statement: [Munduruku people, primaryState, Mato Grosso]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mato Grosso
Context triple: [Munduruku people, primaryState, Mato Grosso]
  • A. Mato Grosso chosen
    Mato Grosso is a large inland state in west-central Brazil known for its vast Amazon rainforest, Pantanal wetlands, and agricultural frontier.
  • B. Rondônia
    Rondônia is a state in northern Brazil known for its Amazon rainforest areas, agricultural frontier, and diverse immigrant communities, including a significant population of German Brazilians.
  • C. Luziânia
    Luziânia is a municipality in the Brazilian state of Goiás, known for its agricultural activities and its role as part of the Brasília metropolitan area.
  • D. Tocantins
    Tocantins is a central Brazilian state known for its relatively recent creation in 1988, its capital Palmas, and its mix of Amazonian and cerrado ecosystems.
  • E. Pará
    Pará is a large state in northern Brazil known for its Amazon rainforest, rich biodiversity, and the major port city of Belém.
  • 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_69e0b4a3f7f48190b37f354574028ca6 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e67854c0448190ab10363f8a218afb completed April 20, 2026, 7:02 p.m.
Created at: April 16, 2026, 11:25 a.m.