Triple

T13525583
Position Surface form Disambiguated ID Type / Status
Subject Torsby Municipality E323008 entity
Predicate locatedInRegion P40 FINISHED
Object northern Värmland E350015 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: northern Värmland | Statement: [Torsby Municipality, locatedInRegion, northern Värmland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: northern Värmland
Context triple: [Torsby Municipality, locatedInRegion, northern Värmland]
  • A. Jämtland region
    Jämtland region is a sparsely populated county in central Sweden known for its lakes, forests, mountains, and outdoor recreation tourism.
  • B. northern Sweden
    Northern Sweden is a sparsely populated, subarctic region known for its vast forests, mountains, and traditional Sámi culture, including reindeer herding and indigenous languages.
  • C. Värmland County chosen
    Värmland County is a region in west-central Sweden known for its vast forests, lakes, and cultural heritage, with Karlstad as its administrative center.
  • D. Västmanland
    Västmanland is a historic province in central Sweden known for its forests, lakes, and long tradition of mining and metallurgy.
  • E. Ångermanland
    Ångermanland is a historical province in northern Sweden known for its deep river valleys, forested landscapes, and coastal areas along the Gulf of Bothnia.
  • 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_69d80766a21881909f21a1b7421d3b8a completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbafa6ad60819087824e4ac83934ed completed April 12, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7549c016c8190945a185b2bc20689 completed May 3, 2026, 1:58 p.m.
Created at: April 9, 2026, 9:44 p.m.