Triple

T3713164
Position Surface form Disambiguated ID Type / Status
Subject Pite Sámi E81462 entity
Predicate traditionalRegion P1968 FINISHED
Object Norrbotten County E346158 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: Norrbotten County | Statement: [Pite Sámi, traditionalRegion, Norrbotten County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Norrbotten County
Context triple: [Pite Sámi, traditionalRegion, Norrbotten County]
  • A. Norrbotten County chosen
    Norrbotten County is Sweden’s northernmost and largest county, known for its Arctic climate, vast wilderness, and sparsely populated landscapes.
  • B. Västerbotten County
    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.
  • 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. Värmland County
    Värmland County is a region in west-central Sweden known for its vast forests, lakes, and cultural heritage, with Karlstad as its administrative center.
  • 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_69ad8b1a81588190b3f27a5483bb610e completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adc9cbc5648190936f93868086167e completed March 8, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5560a42688190b30bce9b7af10db4 completed March 14, 2026, 12:35 p.m.
Created at: March 8, 2026, 3:33 p.m.