Triple
T20731079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cabanagem revolt |
E509572
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Grão-Pará |
—
|
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: Grão-Pará | Statement: [Cabanagem revolt, locatedIn, Grão-Pará]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Grão-Pará Context triple: [Cabanagem revolt, locatedIn, Grão-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.
Maranhão
Maranhão is a northeastern Brazilian state known for its colonial heritage, Afro-Brazilian culture, and the Lençóis Maranhenses dune and lagoon landscapes.
-
D.
Marabá
Marabá is a major industrial and commercial city in southeastern Pará, Brazil, known for its role in mining, steel production, and as a regional transportation hub.
-
E.
Bahia
Bahia is a large and culturally rich state in northeastern Brazil, known for its Afro-Brazilian heritage, historic city of Salvador, and extensive Atlantic coastline.
- 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_69e0b4c589c08190834fb5d86d0efa2b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1ed7e8c8190974f2eb31a0a3106 |
completed | April 21, 2026, 12:16 a.m. |
Created at: April 16, 2026, 12:30 p.m.