Triple

T12524328
Position Surface form Disambiguated ID Type / Status
Subject Lars Magnus Ericsson E299396 entity
Predicate placeOfBirth P1 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: [Lars Magnus Ericsson, placeOfBirth, Värmland County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Värmland County
Context triple: [Lars Magnus Ericsson, placeOfBirth, 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_69d6ada5cdd48190860d9ce30aff69be completed April 8, 2026, 7:33 p.m.
NER Named-entity recognition batch_69d9545c2aa081908e8a5a94d30e23eb completed April 10, 2026, 7:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6a537bb1881908a50073d4f27b66c completed May 3, 2026, 1:30 a.m.
Created at: April 8, 2026, 9:57 p.m.