Triple

T11702808
Position Surface form Disambiguated ID Type / Status
Subject Oktoberfest of Blumenau E278167 entity
Predicate locatedIn P40 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: [Oktoberfest of Blumenau, locatedIn, Blumenau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Blumenau
Context triple: [Oktoberfest of Blumenau, locatedIn, 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. Itajaí
    Itajaí is a coastal city in the Brazilian state of Santa Catarina known for its strong German-Brazilian cultural heritage and important Atlantic port.
  • 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_69ef83525ae081909ee6f3fbb5d37dd7 completed April 27, 2026, 3:40 p.m.
Created at: April 8, 2026, 9:40 p.m.