Triple
T24769488
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roraima |
E619680
|
entity |
| Predicate | hasMunicipalitiesCountApprox |
P30910
|
FINISHED |
| Object | 15 municipalities |
—
|
LITERAL 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: 15 municipalities | Statement: [Roraima, hasMunicipalitiesCountApprox, 15 municipalities]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMunicipalitiesCountApprox Context triple: [Roraima, hasMunicipalitiesCountApprox, 15 municipalities]
-
A.
hasNumberOfMunicipalities
chosen
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
B.
hasMunicipalPart
Indicates that an administrative or territorial entity includes a municipality as one of its constituent parts.
-
C.
hasMunicipalLevel
Indicates that an entity is associated with a specific level or tier within a municipal (local government) hierarchy.
-
D.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
-
E.
hasMunicipalAgglomeration
Indicates that one administrative or geographic entity is part of, or associated with, a larger municipal agglomeration encompassing multiple urban or suburban areas.
- 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_69e2fabd04488190a2d13c97be745a2d |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f47b865df48190bf4b6d3e9f9305e6 |
completed | May 1, 2026, 10:08 a.m. |
| PD | Predicate disambiguation | batch_69f4682c8a3c8190adbfaac99474eaaf |
completed | May 1, 2026, 8:45 a.m. |
Created at: April 18, 2026, 4:29 a.m.