Triple
T11942118
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Paulo metropolitan area |
E284201
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object | Ribeirão Pires |
E368588
|
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: Ribeirão Pires | Statement: [São Paulo metropolitan area, hasMunicipality, Ribeirão Pires]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ribeirão Pires Context triple: [São Paulo metropolitan area, hasMunicipality, Ribeirão Pires]
-
A.
Ribeirão Pires
chosen
Ribeirão Pires is a municipality in the Greater São Paulo metropolitan region of Brazil, known for its green areas and role as a residential and service hub near the state capital.
-
B.
Guaratinguetá
Guaratinguetá is a historic municipality in southeastern Brazil known for its colonial heritage and religious tourism, located in the state of São Paulo.
-
C.
Laranjal Paulista
Laranjal Paulista is a municipality in the state of São Paulo, Brazil, known for its riverside setting and regional agricultural activities.
-
D.
Garça
Garça is the Portuguese term for a heron, a long-legged wading bird commonly found near wetlands and waterways.
-
E.
Sertãozinho
Sertãozinho is a municipality in the interior of Brazil known for its strong sugarcane-based agribusiness and ethanol production.
- 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_69d6ab2db38c8190b1f0ed6663ef8ada |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90342bb908190a019ac91a2b82f3d |
completed | April 10, 2026, 2:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60a5927c4819088f03206561dfd5f |
completed | May 2, 2026, 2:29 p.m. |
Created at: April 8, 2026, 9:45 p.m.