Triple
T12313085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Faculdade de Jaguariúna |
E293529
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Jaguariúna |
E293529
|
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: Jaguariúna | Statement: [Faculdade de Jaguariúna, locatedIn, Jaguariúna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jaguariúna Context triple: [Faculdade de Jaguariúna, locatedIn, Jaguariúna]
-
A.
Jaguariúna
chosen
Jaguariúna is a municipality in southeastern Brazil known for its agribusiness, technology industries, and popular rodeo festival.
-
B.
Mengão
Mengão is the popular nickname of Clube de Regatas do Flamengo, one of Brazil’s most successful and widely supported football clubs.
-
C.
Maracanaú
Maracanaú is an industrial and residential city in northeastern Brazil, located in the metropolitan region of Fortaleza in the state of Ceará.
-
D.
Itatiba
Itatiba is a municipality in southeastern Brazil known for its quality of life and proximity to the metropolitan region of Campinas in the state of São Paulo.
-
E.
Jundiaí
Jundiaí is a mid-sized industrial and logistics city in southeastern Brazil known for its strong economy and high quality of life.
- 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f03d3c88190baedffb83465bff8 |
completed | April 10, 2026, 6:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a9d50b081908f0bdb7a2ca2832a |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:53 p.m.