Triple

T5500057
Position Surface form Disambiguated ID Type / Status
Subject Gnesta Municipality E144303 entity
Predicate subdivisionName2 P766 FINISHED
Object Södermanland E27742 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: Södermanland | Statement: [Gnesta Municipality, subdivisionName2, Södermanland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Södermanland
Context triple: [Gnesta Municipality, subdivisionName2, Södermanland]
  • A. Södermanland County chosen
    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.
  • B. Västmanland
    Västmanland is a historic province in central Sweden known for its forests, lakes, and long tradition of mining and metallurgy.
  • C. Småland
    Småland is a historical province in southern Sweden known for its forests, lakes, traditional red cottages, and as the birthplace of IKEA founder Ingvar Kamprad.
  • D. Uppland
    Uppland is a historical province in east-central Sweden that includes parts of the greater Stockholm area and key infrastructure such as Stockholm Arlanda Airport.
  • E. 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.
  • 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_69c008f5a2748190bce7a39aabf87a6d completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01b921884819082fe30100c71e516 completed March 22, 2026, 4:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07d466cbc8190a91dcc0c26aba817 completed March 22, 2026, 11:37 p.m.
Created at: March 22, 2026, 3:32 p.m.