Triple

T1282579
Position Surface form Disambiguated ID Type / Status
Subject Knivsta Municipality E27359 entity
Predicate locatedInRegion P40 FINISHED
Object Uppland E110264 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: Uppland | Statement: [Knivsta Municipality, locatedInRegion, Uppland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uppland
Context triple: [Knivsta Municipality, locatedInRegion, Uppland]
  • A. Uppland chosen
    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.
  • B. Bohuslän
    Bohuslän is a coastal province in western Sweden known for its rugged granite shoreline, fishing villages, and archipelago along the Skagerrak.
  • C. Blekinge
    Blekinge is a historical province in southern Sweden on the Baltic Sea coast, known for its archipelago, maritime heritage, and strategic location.
  • D. Svealand
    Svealand is the central region of Sweden, historically significant as the country's core area and home to the capital city, Stockholm.
  • 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_69a496d3710c8190955dee8bc0dacb50 completed March 1, 2026, 7:43 p.m.
NER Named-entity recognition batch_69a4c0b47be08190828a1c0a11d94ce8 completed March 1, 2026, 10:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69adf3ab00888190afc7c54d9b89ae3b completed March 8, 2026, 10:09 p.m.
Created at: March 1, 2026, 7:50 p.m.