Triple
T14199871
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mafra, Santa Catarina, Brazil |
E351934
|
entity |
| Predicate | hasBorder |
P224
|
FINISHED |
| Object |
São Bento do Sul
São Bento do Sul is a municipality in the state of Santa Catarina, Brazil, known for its strong German cultural heritage and furniture industry.
|
E1085655
|
NE FINISHED |
How this triple was built (4 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 Bento do Sul | Statement: [Mafra, Santa Catarina, Brazil, hasBorder, São Bento do Sul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: São Bento do Sul Context triple: [Mafra, Santa Catarina, Brazil, hasBorder, São Bento do Sul]
-
A.
Brusque
Brusque is a city in the Brazilian state of Santa Catarina known for its strong German-Brazilian heritage and textile industry.
-
B.
Jaraguá do Sul
Jaraguá do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage and industrial economy.
-
C.
Duas Barras
Duas Barras is a small municipality in the mountainous interior of Rio de Janeiro state in southeastern Brazil.
-
D.
Itanhaém
Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
-
E.
Pelotas
Pelotas is a historic city in southern Brazil known for its colonial architecture, cultural festivals, and traditional sweets industry.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: São Bento do Sul Triple: [Mafra, Santa Catarina, Brazil, hasBorder, São Bento do Sul]
Generated description
São Bento do Sul is a municipality in the state of Santa Catarina, Brazil, known for its strong German cultural heritage and furniture industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: São Bento do Sul Target entity description: São Bento do Sul is a municipality in the state of Santa Catarina, Brazil, known for its strong German cultural heritage and furniture industry.
-
A.
Brusque
Brusque is a city in the Brazilian state of Santa Catarina known for its strong German-Brazilian heritage and textile industry.
-
B.
Jaraguá do Sul
Jaraguá do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage and industrial economy.
-
C.
Duas Barras
Duas Barras is a small municipality in the mountainous interior of Rio de Janeiro state in southeastern Brazil.
-
D.
Itanhaém
Itanhaém is a coastal municipality in southeastern Brazil known for its beaches, historic colonial center, and tourism along the São Paulo state shoreline.
-
E.
Pelotas
Pelotas is a historic city in southern Brazil known for its colonial architecture, cultural festivals, and traditional sweets industry.
- F. None of above. chosen
Provenance (5 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_69d827894ac0819097803e57f3227b23 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de61f472548190a1a7edc40526eac3 |
completed | April 14, 2026, 3:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd194f13688190af7ef5ceb92a73ff |
completed | May 7, 2026, 10:59 p.m. |
| NEDg | Description generation | batch_69fd1b198c6c81909b71a51a39711754 |
completed | May 7, 2026, 11:07 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd1bb585448190bc3393304980b808 |
completed | May 7, 2026, 11:09 p.m. |
Created at: April 10, 2026, 1:04 a.m.