Triple
T17680863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amazon Time |
E440764
|
entity |
| Predicate | usedInState |
P35332
|
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: [Amazon Time, usedInState, Mato Grosso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mato Grosso Context triple: [Amazon Time, usedInState, 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_69d8b9e940b081908b862bb0e6e89b0d |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e470445b3881908bb0930b986089f7 |
completed | April 19, 2026, 6:03 a.m. |
Created at: April 10, 2026, 10:01 a.m.