Triple
T12313246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Paulo State University |
E293534
|
entity |
| Predicate | hasCampusIn |
P4623
|
FINISHED |
| Object | Bauru |
E278515
|
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: Bauru | Statement: [São Paulo State University, hasCampusIn, Bauru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bauru Context triple: [São Paulo State University, hasCampusIn, Bauru]
-
A.
Bauru
chosen
Bauru is a city in the state of São Paulo, Brazil, known as a regional economic and educational hub that hosts a campus of the University of São Paulo.
-
B.
Barueri
Barueri is a rapidly developing municipality in the São Paulo metropolitan area of Brazil, known for its strong commercial sector and high standard of living.
-
C.
Ribeirão Preto
Ribeirão Preto is a major city in the state of São Paulo, Brazil, known as an important economic and cultural center with a strong agribusiness and services sector.
-
D.
Guarulhos
Guarulhos is a major city in the São Paulo metropolitan area of Brazil, known as an important industrial and logistics hub.
-
E.
São Carlos
São Carlos is a Brazilian city in the state of São Paulo known as a major university and technology hub, hosting important campuses and research centers.
- 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_69f7304d8ad08190bf444835cfca6205 |
completed | May 3, 2026, 11:23 a.m. |
Created at: April 8, 2026, 9:53 p.m.