Triple
T11831280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vale do Taquari |
E281397
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Lajeado |
E281396
|
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: Lajeado | Statement: [Vale do Taquari, hasCity, Lajeado]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lajeado Context triple: [Vale do Taquari, hasCity, Lajeado]
-
A.
Lajeado
chosen
Lajeado is a city in southern Brazil known for its strong German-Brazilian cultural heritage and traditions.
-
B.
Baraguá
Baraguá is a historic locality in eastern Cuba best known as the site of the 1878 Baraguá Protest, a landmark act of resistance during the Ten Years' War for Cuban independence.
-
C.
Caxangá
Caxangá is a neighborhood and important urban area within the city of Recife, Brazil.
-
D.
Ponta Porã
Ponta Porã is a Brazilian border city in the state of Mato Grosso do Sul, known for its close integration with the Paraguayan city of Pedro Juan Caballero.
-
E.
Estância
Estância is a municipality in the Brazilian state of Sergipe, known for its coastal location and traditional June festivals.
- 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_69d6ab276f8c8190b1966a0ef11349ac |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a62c95988190a45dbaa7001c8846 |
completed | April 10, 2026, 7:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f41787c08c8190a3427aaf2f639889 |
completed | May 1, 2026, 3:01 a.m. |
Created at: April 8, 2026, 9:43 p.m.