Triple

T1568604
Position Surface form Disambiguated ID Type / Status
Subject Gävleborg County E33488 entity
Predicate borders P224 FINISHED
Object Dalarna County E72579 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: Dalarna County | Statement: [Gävleborg County, borders, Dalarna County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dalarna County
Context triple: [Gävleborg County, borders, Dalarna County]
  • A. Dalarna chosen
    Dalarna is a historical province in central Sweden known for its distinct cultural traditions, including unique dialects, folk costumes, and the iconic Dala horse.
  • B. Västmanland County
    Västmanland County is an administrative region in central Sweden known for its mix of industrial towns, forests, and lakes.
  • C. Gävleborg County
    Gävleborg County is a region in east-central Sweden along the Baltic coast, known for its mix of industrial towns, forests, and coastal landscapes.
  • D. Uppsala County
    Uppsala County is an administrative region in east-central Sweden known for its historic university city of Uppsala and its mix of cultural heritage and rural landscapes.
  • E. Östergötland County
    Östergötland County is an administrative region in southeastern Sweden known for its mix of historic cities, fertile plains, and coastal and archipelago landscapes along the Baltic Sea.
  • 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_69a885f11b048190935025a035302715 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a908a15e308190b8bec55d1712812a completed March 5, 2026, 4:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69af83294c4881908411bc4f8649dbdd completed March 10, 2026, 2:34 a.m.
Created at: March 4, 2026, 7:27 p.m.