Triple
T14886732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Potenza |
E350143
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object | Blumenau |
E278154
|
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: Blumenau | Statement: [Potenza, twinnedWith, Blumenau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Blumenau Context triple: [Potenza, twinnedWith, Blumenau]
-
A.
Blumenau
chosen
Blumenau is a city in southern Brazil known for its strong German cultural heritage, architecture, and one of the world’s largest Oktoberfest celebrations.
-
B.
Brusque
Brusque is a city in the Brazilian state of Santa Catarina known for its strong German-Brazilian heritage and textile industry.
-
C.
Pelotas
Pelotas is a historic city in southern Brazil known for its colonial architecture, cultural festivals, and traditional sweets industry.
-
D.
Jaraguá do Sul
Jaraguá do Sul is a city in southern Brazil known for its strong German-Brazilian cultural heritage and industrial economy.
-
E.
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.
- 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_69d822ee4f408190b6ac3b2fa434f0df |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded5f5b1c88190815f3585770cb135 |
completed | April 15, 2026, 12:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6b5f22c08190a9530cbd78cfc801 |
completed | May 8, 2026, 11:01 p.m. |
Created at: April 10, 2026, 1:56 a.m.