Triple

T1176844
Position Surface form Disambiguated ID Type / Status
Subject Sigtuna Municipality E25045 entity
Predicate subdivisionName2 P766 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: [Sigtuna Municipality, subdivisionName2, Uppland]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Uppland
Context triple: [Sigtuna Municipality, subdivisionName2, 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_69a494267b4c819088c97a59182bf56a completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bd0ebbd08190a441d16a5b65a15e completed March 1, 2026, 10:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69ada0b527dc819085f7b3ff85170e16 completed March 8, 2026, 4:15 p.m.
Created at: March 1, 2026, 7:45 p.m.