Triple
T12294354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guarulhos |
E293044
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Mairiporã |
E299856
|
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: Mairiporã | Statement: [Guarulhos, borderedBy, Mairiporã]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mairiporã Context triple: [Guarulhos, borderedBy, Mairiporã]
-
A.
Mairiporã
chosen
Mairiporã is a municipality in southeastern Brazil known for its mountainous landscapes, proximity to the Cantareira State Park, and role as a green retreat near the São Paulo metropolitan area.
-
B.
Porcari
Porcari is a small Italian town and comune in Tuscany, known for its industrial and agricultural activities within the Province of Lucca.
-
C.
Itapura
Itapura is a municipality in the state of São Paulo, Brazil, located on the banks of the Tietê River near its confluence with the Paraná River.
-
D.
Pirassununga
Pirassununga is a municipality in the state of São Paulo, Brazil, known for its agricultural activities and as a site of a major University of São Paulo campus.
-
E.
Votuporanga
Votuporanga is a municipality in northwestern São Paulo state in Brazil, known as a regional commercial and service center with a strong furniture and industrial sector.
- 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_69d6ab690ad081908c0ed3870ec82d53 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93ed7251c8190b94d7cd75ad49b9c |
completed | April 10, 2026, 6:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e79bf548190bf7f314222ed1ed1 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:52 p.m.