Triple

T8808888
Position Surface form Disambiguated ID Type / Status
Subject Nyland E209603 entity
Predicate successorAdministrativeUnit P6576 FINISHED
Object Nyland County (Finland) E209603 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: Nyland County (Finland) | Statement: [Nyland, successorAdministrativeUnit, Nyland County (Finland)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nyland County (Finland)
Context triple: [Nyland, successorAdministrativeUnit, Nyland County (Finland)]
  • A. Kexholm County
    Kexholm County was a historical province of the Swedish Empire in the Karelian region, later ceded to the Russian Empire and incorporated into its northwestern territories.
  • B. Põlva County
    Põlva County is a rural administrative region in southeastern Estonia known for its forests, lakes, and strong South Estonian cultural and linguistic heritage.
  • C. Viljandi County
    Viljandi County is a rural administrative region in southern Estonia known for its lakes, forests, and historic town of Viljandi.
  • D. Nyland chosen
    Nyland is the historical Swedish name for the coastal region of southern Finland now known as Uusimaa.
  • E. Järva County
    Järva County is a historical and administrative region in central Estonia known for its rural landscapes and small towns.
  • 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_69ca8363f3308190a47e3f1ebd51f613 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5fd4cbec8190a929d4e60da8ad65 completed March 31, 2026, 11:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf6fa0e4308190bd01c2d107c8c02d completed April 3, 2026, 7:43 a.m.
Created at: March 30, 2026, 6:45 p.m.