Triple

T1657180
Position Surface form Disambiguated ID Type / Status
Subject Valbo E35825 entity
Predicate locatedIn P40 FINISHED
Object Gävleborg County E33488 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: Gävleborg County | Statement: [Valbo, locatedIn, Gävleborg County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gävleborg County
Context triple: [Valbo, locatedIn, Gävleborg County]
  • A. Gävleborg County chosen
    Gävleborg County is a region in east-central Sweden along the Baltic coast, known for its mix of industrial towns, forests, and coastal landscapes.
  • B. Östergötland County
    Östergötland County is an administrative region in southeastern Sweden known for its mix of historic cities, fertile plains, and coastal and archipelago landscapes along the Baltic Sea.
  • C. Örebro County
    Örebro County is a county in central Sweden known for its industrial heritage, including major arms manufacturer Bofors, and its administrative center in the city of Örebro.
  • D. Västernorrland County
    Västernorrland County is a coastal county in northern Sweden known for its forests, rivers, and towns such as Sundsvall and Härnösand.
  • E. 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.
  • 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_69a88606aa808190aa0b421b4271f220 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a8d85f08190a50ade3f443b7703 completed March 5, 2026, 4:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf15a4248190b898e3bfbeb2997a completed March 10, 2026, 5:41 a.m.
Created at: March 4, 2026, 7:29 p.m.