Triple
T12313256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Paulo State University |
E293534
|
entity |
| Predicate | hasCampusIn |
P4623
|
FINISHED |
| Object | Guaratinguetá |
E357430
|
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: Guaratinguetá | Statement: [São Paulo State University, hasCampusIn, Guaratinguetá]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Guaratinguetá Context triple: [São Paulo State University, hasCampusIn, Guaratinguetá]
-
A.
Guaratinguetá
chosen
Guaratinguetá is a historic municipality in southeastern Brazil known for its colonial heritage and religious tourism, located in the state of São Paulo.
-
B.
Taquaritinga
Taquaritinga is a municipality in the interior of Brazil’s São Paulo state, known for its agricultural production and regional commerce.
-
C.
Jaboticabal
Jaboticabal is a municipality in the state of São Paulo, Brazil, known for its strong agricultural economy and educational institutions.
-
D.
Itapetininga
Itapetininga is a municipality in southeastern Brazil known for its agricultural activities and regional commercial importance within the state of São Paulo.
-
E.
Guarujá
Guarujá is a coastal resort city in southeastern Brazil known for its popular beaches and tourism.
- 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_69f75d78433081909ae0278e9e1abacb |
completed | May 3, 2026, 2:36 p.m. |
Created at: April 8, 2026, 9:53 p.m.