Triple
T11795855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vale do Itajaí |
E280503
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object | Brusque |
E284978
|
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: Brusque | Statement: [Vale do Itajaí, hasMajorCity, Brusque]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brusque Context triple: [Vale do Itajaí, hasMajorCity, Brusque]
-
A.
Brusque
chosen
Brusque is a city in the Brazilian state of Santa Catarina known for its strong German-Brazilian heritage and textile industry.
-
B.
Jaraguá do Sul
Jaraguá do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage and industrial economy.
-
C.
Duas Barras
Duas Barras is a small municipality in the mountainous interior of Rio de Janeiro state in southeastern Brazil.
-
D.
Itajaí
Itajaí is a coastal city in the Brazilian state of Santa Catarina known for its strong German-Brazilian cultural heritage and important Atlantic port.
-
E.
Maringá
Maringá is a planned, mid-20th-century city in the state of Paraná known for its green urban design, strong agricultural-based economy, and high quality of life.
- 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_69d6ab258b808190b1735835c841e3a4 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a5a1cda0819092d66a82fd882786 |
completed | April 10, 2026, 7:24 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f45825e9ac8190ad13d4b4e0208d20 |
completed | May 1, 2026, 7:37 a.m. |
Created at: April 8, 2026, 9:42 p.m.