Triple
T11831211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | São Leopoldo |
E281395
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object | São Leopoldo, RS |
E281395
|
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: São Leopoldo, RS | Statement: [São Leopoldo, abbreviation, São Leopoldo, RS]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São Leopoldo, RS Context triple: [São Leopoldo, abbreviation, São Leopoldo, RS]
-
A.
São Leopoldo
chosen
São Leopoldo is a city in southern Brazil historically recognized as a major center of German immigration and culture in the country.
-
B.
Novo Hamburgo
Novo Hamburgo is a city in southern Brazil known for its strong German immigrant heritage and influential role in the country’s footwear industry.
-
C.
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.
-
D.
Três Rios
Três Rios is a municipality in the state of Rio de Janeiro, Brazil, known as a regional commercial and logistical hub at the confluence of three rivers.
-
E.
Canoas
Canoas is a major industrial and residential city in the Porto Alegre metropolitan region of Rio Grande do Sul, Brazil.
- 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_69f16741d9a08190b6d6d5e59dfa41b8 |
completed | April 29, 2026, 2:04 a.m. |
Created at: April 8, 2026, 9:43 p.m.