Triple
T20790280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sinos River |
E511754
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Novo Hamburgo |
—
|
NE NERFINISHED |
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: Novo Hamburgo | Statement: [Sinos River, passesNear, Novo Hamburgo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Novo Hamburgo Context triple: [Sinos River, passesNear, Novo Hamburgo]
-
A.
Novo Hamburgo
chosen
Novo Hamburgo is a city in southern Brazil known for its strong German immigrant heritage and influential role in the country’s footwear industry.
-
B.
São Leopoldo
São Leopoldo is a city in southern Brazil historically recognized as a major center of German immigration and culture in the country.
-
C.
Canoas
Canoas is a major industrial and residential city in the Porto Alegre metropolitan region of Rio Grande do Sul, Brazil.
-
D.
Teresópolis
Teresópolis is a mountainous city in the state of Rio de Janeiro, Brazil, known for its cool climate, natural parks, and role as a popular ecotourism and weekend getaway destination.
-
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 (2 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_69e0b4cb83948190bd57bec21d78ed53 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c28f0e1081909f18dddfc6ec084c |
completed | April 21, 2026, 12:19 a.m. |
Created at: April 16, 2026, 12:38 p.m.