Triple
T6217580
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bertioga |
E139027
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Salesópolis |
E534584
|
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: Salesópolis | Statement: [Bertioga, borderedBy, Salesópolis]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salesópolis Context triple: [Bertioga, borderedBy, Salesópolis]
-
A.
Salesópolis
chosen
Salesópolis is a municipality in the state of São Paulo, Brazil, known for its Atlantic Forest landscapes and for being the headwaters region of the Tietê River.
-
B.
Uberlândia
Uberlândia is a major commercial and logistics hub in the Brazilian state of Minas Gerais, known for its agribusiness, services sector, and strategic location in the country's Southeast.
-
C.
Avaré
Avaré is a municipality in the interior of Brazil known for its agricultural activities and proximity to the Jurumirim Reservoir, located in the state of São Paulo.
-
D.
Itanhaém
Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
-
E.
Osasco
Osasco is a major industrial and commercial city in the metropolitan region of São Paulo, Brazil.
- 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_69c008aecb0c81909984b48f733ce8ae |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062a35e308190be25c41b02704411 |
completed | March 22, 2026, 9:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c20db4e0ac8190ba7bca1f9d8ac6df |
completed | March 24, 2026, 4:06 a.m. |
Created at: March 22, 2026, 4:21 p.m.