Triple
T11911862
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barueri |
E283414
|
entity |
| Predicate | hasNeighbour |
P5707
|
FINISHED |
| Object | Cotia |
E329369
|
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: Cotia | Statement: [Barueri, hasNeighbour, Cotia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cotia Context triple: [Barueri, hasNeighbour, Cotia]
-
A.
Cotia
chosen
Cotia is a municipality in the metropolitan region of São Paulo, Brazil, known for its residential areas, green spaces, and proximity to the capital city.
-
B.
Itaquaquecetuba
Itaquaquecetuba is a municipality in the Greater São Paulo metropolitan area of southeastern Brazil, known for its rapid urban growth and industrial activity.
-
C.
Araruama
Araruama is a coastal municipality in the state of Rio de Janeiro, Brazil, known for its large saltwater lagoon and tourism.
-
D.
Nilópolis
Nilópolis is a densely populated municipality in the state of Rio de Janeiro, Brazil, known for its urban character and strong cultural ties to the Rio de Janeiro metropolitan area.
-
E.
Itanhaém
Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
- 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e528f6748190ac873a040a61fa93 |
completed | April 10, 2026, 11:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f44014e1d08190ac7425f375ca023f |
completed | May 1, 2026, 5:54 a.m. |
Created at: April 8, 2026, 9:44 p.m.