Triple

T7657780
Position Surface form Disambiguated ID Type / Status
Subject Helsingborg E173429 entity
Predicate locatedIn P40 FINISHED
Object Skåne County E300551 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: Skåne County | Statement: [Helsingborg, locatedIn, Skåne County]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Skåne County
Context triple: [Helsingborg, locatedIn, Skåne County]
  • A. Skåne County chosen
    Skåne County is Sweden’s southernmost county, known for its fertile farmland, coastal landscapes, and major cities such as Malmö and Lund.
  • 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. Västra Götaland County
    Västra Götaland County is a large administrative region in western Sweden that includes the city of Gothenburg and serves as an important hub for industry, culture, and transportation.
  • D. Blekinge
    Blekinge is a historical province in southern Sweden on the Baltic Sea coast, known for its archipelago, maritime heritage, and strategic location.
  • 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_69c69955517c819085bc715b96d304d2 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c7019161548190855a5b1e9f5d7e99 completed March 27, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c9fc2e796c81908e291a11f239b9fc completed March 30, 2026, 4:29 a.m.
Created at: March 27, 2026, 3:59 p.m.