Triple
T3535879
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mato Grosso |
E74770
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Pará |
E169954
|
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: Pará | Statement: [Mato Grosso, borderedBy, Pará]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pará Context triple: [Mato Grosso, borderedBy, Pará]
-
A.
Pará
chosen
Pará is a large state in northern Brazil known for its Amazon rainforest, rich biodiversity, and the major port city of Belém.
-
B.
Amapá
Amapá is a sparsely populated state in northern Brazil, located in the Amazon region along the Atlantic coast and bordering French Guiana.
-
C.
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.
-
D.
Paraná state
Paraná state is a southern Brazilian state known for its diverse landscapes, major agricultural production, and popular natural attractions including part of the Iguaçu National Park.
-
E.
Bahia
Bahia is a traditional Brazilian football club based in Salvador, known for its passionate fanbase and historic success in national competitions.
- 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_69b44eeb831481909f9def02fe995c69 |
completed | March 13, 2026, 5:52 p.m. |
Created at: March 8, 2026, 3:20 p.m.