Triple

T7994422
Position Surface form Disambiguated ID Type / Status
Subject Bollnäs E186086 entity
Predicate countrySubdivision P766 FINISHED
Object Hälsingland E238313 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: Hälsingland | Statement: [Bollnäs, countrySubdivision, Hälsingland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hälsingland
Context triple: [Bollnäs, countrySubdivision, Hälsingland]
  • A. Hälsingland chosen
    Hälsingland is a historical province in central Sweden known for its traditional decorated farmhouses, forests, and cultural heritage.
  • B. 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.
  • C. Uppland
    Uppland is a historical province in east-central Sweden that includes parts of the greater Stockholm area and key infrastructure such as Stockholm Arlanda Airport.
  • D. Bohuslän
    Bohuslän is a coastal province in western Sweden known for its rugged granite shoreline, fishing villages, and archipelago along the Skagerrak.
  • E. Dalsland
    Dalsland is a historical province in western Sweden known for its forests, lakes, and rural 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_69ca829c6c308190ab05b43d234c52b2 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c73ba388190bcedc29fbdd22f3c completed March 31, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc63bcae048190a3fd151b2d8f9f77 completed April 1, 2026, 12:15 a.m.
Created at: March 30, 2026, 5:16 p.m.