Triple

T7541520
Position Surface form Disambiguated ID Type / Status
Subject Gästrikland E178285 entity
Predicate borders P224 FINISHED
Object Västmanland E248581 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: Västmanland | Statement: [Gästrikland, borders, Västmanland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Västmanland
Context triple: [Gästrikland, borders, Västmanland]
  • A. Västmanland chosen
    Västmanland is a historic province in central Sweden known for its forests, lakes, and long tradition of mining and metallurgy.
  • B. Västmanland County
    Västmanland County is an administrative region in central Sweden known for its mix of industrial towns, forests, and lakes.
  • C. Södermanland County
    Södermanland County is an administrative region in east-central Sweden known for its mix of coastal landscapes, forests, and historic towns such as Nyköping and Eskilstuna.
  • 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. Västernorrland County
    Västernorrland County is a coastal county in northern Sweden known for its forests, rivers, and towns such as Sundsvall and Härnösand.
  • 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_69c69f2be3888190a6667a27f8f195e9 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f8750f80819088ddfb7a5580b5df completed March 27, 2026, 9:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c97ca3d5e0819097e904184202b75f completed March 29, 2026, 7:25 p.m.
Created at: March 27, 2026, 3:48 p.m.