Triple
T21468992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laranjal Paulista |
E529672
|
entity |
| Predicate | subdivisionName |
P747
|
FINISHED |
| Object | Laranjal Paulista |
—
|
NE NERFINISHED |
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: Laranjal Paulista | Statement: [Laranjal Paulista, subdivisionName, Laranjal Paulista]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laranjal Paulista Context triple: [Laranjal Paulista, subdivisionName, Laranjal Paulista]
-
A.
Laranjal Paulista
chosen
Laranjal Paulista is a municipality in the state of São Paulo, Brazil, known for its riverside setting and regional agricultural activities.
-
B.
Bragança Paulista
Bragança Paulista is a municipality in southeastern Brazil known for its historical architecture, mild climate, and role as a regional commercial and educational center.
-
C.
Sertãozinho
Sertãozinho is a municipality in the interior of Brazil known for its strong sugarcane-based agribusiness and ethanol production.
-
D.
Taquaritinga
Taquaritinga is a municipality in the interior of Brazil’s São Paulo state, known for its agricultural production and regional commerce.
-
E.
Jaboticabal
Jaboticabal is a municipality in the state of São Paulo, Brazil, known for its strong agricultural economy and educational institutions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c459acb481909bb6ee452a0045c7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9e9f58f6c8190a3d2fc8f820a9925 |
completed | April 23, 2026, 9:44 a.m. |
Created at: April 16, 2026, 6:16 p.m.