Triple

T8677425
Position Surface form Disambiguated ID Type / Status
Subject Österbottens landskap E205950 entity
Predicate containsMunicipality P852 FINISHED
Object Korsholm E266054 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: Korsholm | Statement: [Österbottens landskap, containsMunicipality, Korsholm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Korsholm
Context triple: [Österbottens landskap, containsMunicipality, Korsholm]
  • A. Korsholm chosen
    Korsholm is a coastal municipality in western Finland, known for its largely Swedish-speaking population and proximity to the city of Vaasa in the Ostrobothnia region.
  • B. Rudkøbing
    Rudkøbing is a small historic town on the Danish island of Langeland, known for its well-preserved old streets and as the birthplace of physicist Hans Christian Ørsted.
  • C. Næstved
    Næstved is a historic market town and commercial center in southern Denmark, located on the island of Zealand.
  • D. Oksbøl
    Oksbøl is a town in southwestern Jutland, Denmark, known for its military training areas and historical role as a garrison location.
  • E. Hellebæk
    Hellebæk is a coastal town in northeastern Zealand, Denmark, known for its scenic setting near Helsingør and its historic industrial and residential architecture.
  • 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_69ca83529a9c8190b5c075b4f14636ed completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc49f7c2c081909ec93413ceefbb1c completed March 31, 2026, 10:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69d178a6467c8190aab201fb12d2a64e completed April 4, 2026, 8:46 p.m.
Created at: March 30, 2026, 6:32 p.m.