Triple
T30185921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macuxi |
E767334
|
entity |
| Predicate | mainStateInBrazil |
P157696
|
FINISHED |
| Object | Roraima |
—
|
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: Roraima | Statement: [Macuxi, mainStateInBrazil, Roraima]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainStateInBrazil Context triple: [Macuxi, mainStateInBrazil, Roraima]
-
A.
mainAreaInBrazil
chosen
Indicates that a given area or region is the primary or most significant area located within Brazil for a specified entity or context.
-
B.
largestPopulationInBrazilianState
Indicates that the subject has the largest population among all entities within a specified Brazilian state.
-
C.
statusInBrazil
Indicates the legal, social, or operational status that an entity holds specifically within the context of Brazil.
-
D.
significantPopulationInBrazilianState
Indicates that a population group or entity has a notably large or important presence within a specific Brazilian state.
-
E.
urbanAreaRankInBrazil
Indicates the relative position or ranking of an urban area compared to other urban areas within Brazil.
- F. None of above.
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_69f2247cc3d88190811dec3face94bf5 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6e6029a10819098ff21f58079e70e |
completed | May 3, 2026, 6:06 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d5e8188190b1e1c2e5d1b77031 |
completed | May 3, 2026, 5:57 a.m. |
Created at: April 29, 2026, 7:27 p.m.