Triple
T13067658
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cotia |
E329369
|
entity |
| Predicate | borderingEntity |
P224
|
FINISHED |
| Object | Carapicuíba |
E325452
|
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: Carapicuíba | Statement: [Cotia, borderingEntity, Carapicuíba]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Carapicuíba Context triple: [Cotia, borderingEntity, Carapicuíba]
-
A.
Carapicuíba
chosen
Carapicuíba is a densely populated municipality in the São Paulo metropolitan area in southeastern Brazil.
-
B.
Jundiaí
Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
-
C.
Osasco
Osasco is a major industrial and commercial city in the metropolitan region of São Paulo, Brazil.
-
D.
Mogi Guaçu
Mogi Guaçu is a municipality in the interior of Brazil’s São Paulo state, known for its industrial activity and the Mogi Guaçu River that runs through it.
-
E.
Uberaba
Uberaba is a mid-sized Brazilian city in the western part of Minas Gerais state, known for its strong agribusiness sector and cattle breeding traditions.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980ec8ba48190baf52c7823482680 |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6d603419c8190b8d1726365db59dc |
completed | May 3, 2026, 4:58 a.m. |
Created at: April 9, 2026, 9 p.m.