Triple

T5351302
Position Surface form Disambiguated ID Type / Status
Subject Selma Lagerlöf E102583 entity
Predicate placeOfDeath P21 FINISHED
Object Värmland County 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: Värmland County | Statement: [Selma Lagerlöf, placeOfDeath, Värmland County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Värmland County
Context triple: [Selma Lagerlöf, placeOfDeath, Värmland County]
  • A. 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.
  • B. Jämtland County
    Jämtland County is a large, sparsely populated region in central Sweden known for its mountains, forests, and popular outdoor tourism 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. 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.
  • E. Västmanland County
    Västmanland County is an administrative region in central Sweden known for its mix of industrial towns, forests, and lakes.
  • 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_69bd43d8f7248190b64c140734b5c9a8 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd861188ac81908ef2b1f25cc6c864 completed March 20, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfb70320ac819088ba3b1da868d9a4 completed March 22, 2026, 9:31 a.m.
Created at: March 20, 2026, 2:01 p.m.