Triple
T12223367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Santa Catarina |
E291275
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Chapecó |
E651373
|
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: Chapecó | Statement: [Santa Catarina, hasCity, Chapecó]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chapecó Context triple: [Santa Catarina, hasCity, Chapecó]
-
A.
Chapecó
chosen
Chapecó is a major city in the state of Santa Catarina known as an important regional hub for agribusiness and food processing in southern Brazil.
-
B.
Catanduva
Catanduva is a municipality in the northwestern region of the state of São Paulo, Brazil, known for its agricultural production and regional commercial importance.
-
C.
Yacuiba
Yacuiba is a city in southern Bolivia near the Argentine border, known as a key commercial and transportation hub in the Gran Chaco region.
-
D.
Caxias do Sul
Caxias do Sul is a major city in southern Brazil known for its strong European immigrant heritage, particularly German and Italian influences, and its significant industrial and wine-producing sectors.
-
E.
Gualeguaychú
Gualeguaychú is a city in eastern Argentina known for its vibrant Carnival celebrations and riverside tourism.
- 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_69d6ab668acc8190963ba424049d6aee |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91ca11f788190bad2efb6c83ffccb |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f60aa6d3d481909852a6f2f90d7a41 |
completed | May 2, 2026, 2:31 p.m. |
Created at: April 8, 2026, 9:51 p.m.