Triple
T21533066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Federal University of Mato Grosso |
E531282
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Cuiabá |
—
|
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: Cuiabá | Statement: [Federal University of Mato Grosso, headquartersLocation, Cuiabá]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cuiabá Context triple: [Federal University of Mato Grosso, headquartersLocation, 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.
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.
Ponta Porã
Ponta Porã is a Brazilian border city in the state of Mato Grosso do Sul, known for its close integration with the Paraguayan city of Pedro Juan Caballero.
-
D.
Cascavel
Cascavel is a major city in western Paraná, Brazil, known as an important regional hub for agribusiness, commerce, and services.
-
E.
Araguaína
Araguaína is a major commercial and economic center in northern Brazil, located in the state of Tocantins.
- 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_69e0c45e5b8881908ac18fc2f493b114 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ee9d0ae0e88190a6042effd93cd455 |
completed | April 26, 2026, 11:17 p.m. |
Created at: April 16, 2026, 6:27 p.m.