Triple
T6711592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central-West Region of Brazil |
E153156
|
entity |
| Predicate | includesCity |
P3207
|
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: [Central-West Region of Brazil, includesCity, Cuiabá]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cuiabá Context triple: [Central-West Region of Brazil, includesCity, 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.
Planaltina
Planaltina is one of the administrative regions of Brazil’s Federal District, known for its historic town center and role as a residential area near Brasília.
-
E.
Lajeado
Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
- 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_69c68808d8d8819087369015270788fe |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d108acc08190b38b43161d8912b9 |
completed | March 27, 2026, 6:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c700924fdc8190990187fbc1b3ab20 |
completed | March 27, 2026, 10:11 p.m. |
Created at: March 27, 2026, 2:06 p.m.