Triple
T11703244
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Serra Gaúcha |
E278175
|
entity |
| Predicate | hasMajorTown |
P316
|
FINISHED |
| Object | Gramado |
E847879
|
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: Gramado | Statement: [Serra Gaúcha, hasMajorTown, Gramado]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gramado Context triple: [Serra Gaúcha, hasMajorTown, Gramado]
-
A.
Gramado
chosen
Gramado is a popular mountain resort town in southern Brazil known for its European-style architecture, chocolate shops, and major tourist festivals such as Natal Luz.
-
B.
Passo Fundo
Passo Fundo is a mid-sized city in southern Brazil known as a regional economic, educational, and healthcare hub in the state of Rio Grande do Sul.
-
C.
Canoas
Canoas is a major industrial and residential city in the Porto Alegre metropolitan region of Rio Grande do Sul, Brazil.
-
D.
Pelotas
Pelotas is a historic city in southern Brazil known for its colonial architecture, cultural festivals, and traditional sweets industry.
-
E.
Jaraguá do Sul
Jaraguá do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage and industrial economy.
- 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_69d6aaff2ce88190b4a1e4b341ad5377 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a49b1080819096593733ee48a187 |
completed | April 10, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f0195739348190b40a378ca227cf85 |
completed | April 28, 2026, 2:20 a.m. |
Created at: April 8, 2026, 9:40 p.m.