Triple

T8789159
Position Surface form Disambiguated ID Type / Status
Subject Salo, Finland E209115 entity
Predicate hasTwinTown P919 FINISHED
Object Sønderborg, Denmark E521588 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: Sønderborg, Denmark | Statement: [Salo, Finland, hasTwinTown, Sønderborg, Denmark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sønderborg, Denmark
Context triple: [Salo, Finland, hasTwinTown, Sønderborg, Denmark]
  • A. Sønderborg chosen
    Sønderborg is a coastal town in southern Denmark known for its historic castle, waterfront setting on the island of Als, and role as a regional cultural and educational center.
  • B. Billund, Denmark
    Billund, Denmark is a small Danish town best known as the birthplace of LEGO and home to the original LEGOLAND theme park.
  • C. Farum, Denmark
    Farum, Denmark is a suburban town in Furesø Municipality on the island of Zealand, known for its residential character and proximity to Copenhagen.
  • D. Svendborg
    Svendborg is a historic coastal town and seaport in southern Denmark known for its maritime heritage and location on the island of Funen.
  • E. Karup, Denmark
    Karup, Denmark is a village in central Jutland best known as a major military hub and home to the primary air base of the Royal Danish Air Force.
  • 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_69ca836168108190bb43d3dc235c1f55 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5f8b0c108190af53d4bb9b132c5c completed March 31, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf5210454c8190aa83d941893a4bc5 completed April 3, 2026, 5:37 a.m.
Created at: March 30, 2026, 6:43 p.m.