Triple

T9638239
Position Surface form Disambiguated ID Type / Status
Subject Umeå E232990 entity
Predicate isRegionalCapitalOf P3877 FINISHED
Object Västerbotten County E348319 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: Västerbotten County | Statement: [Umeå, isRegionalCapitalOf, Västerbotten County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Västerbotten County
Context triple: [Umeå, isRegionalCapitalOf, Västerbotten County]
  • A. Västerbotten County chosen
    Västerbotten County is a large administrative region in northern Sweden known for its vast forests, coastline along the Gulf of Bothnia, and sparsely populated inland areas.
  • B. Norrbotten County
    Norrbotten County is Sweden’s northernmost and largest county, known for its Arctic climate, vast wilderness, and sparsely populated landscapes.
  • C. Västernorrland County
    Västernorrland County is a coastal county in northern Sweden known for its forests, rivers, and towns such as Sundsvall and Härnösand.
  • D. Jämtland County
    Jämtland County is a large, sparsely populated region in central Sweden known for its mountains, forests, and popular outdoor tourism areas.
  • E. 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.
  • 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_69ca848a5a908190aad251f4137b0c3a completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9b51b08081908e30744607b28953 completed April 1, 2026, 10:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69d189fd53fc8190bc36f2b8e0e21036 completed April 4, 2026, 10 p.m.
Created at: March 30, 2026, 8:11 p.m.