Triple
T2720375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of São Paulo |
E60066
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | São Carlos |
E272089
|
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: São Carlos | Statement: [State of São Paulo, hasCity, São Carlos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São Carlos Context triple: [State of São Paulo, hasCity, São Carlos]
-
A.
São Carlos
chosen
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.
-
B.
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.
-
C.
Bauru
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.
-
D.
São José do Rio Preto
São José do Rio Preto is a major inland city in northwestern São Paulo state, Brazil, known as a regional economic, medical, and educational hub.
-
E.
Sorocaba
Sorocaba is a major industrial and commercial city in southeastern Brazil, located in the interior of the state of São Paulo.
- 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_69ab4b746d248190958e052045c09255 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdab06d388190acf690787fe58ab5 |
completed | March 7, 2026, 7:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b22496fda88190ba70356cc6835dcb |
completed | March 12, 2026, 2:27 a.m. |
Created at: March 6, 2026, 9:55 p.m.