Triple

T21888291
Position Surface form Disambiguated ID Type / Status
Subject Sofienberg E540470 entity
Predicate hasPark P105 FINISHED
Object Sofienbergparken NE NERFINISHED

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: Sofienbergparken | Statement: [Sofienberg, hasPark, Sofienbergparken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sofienbergparken
Context triple: [Sofienberg, hasPark, Sofienbergparken]
  • A. Sofienbergparken chosen
    Sofienbergparken is a popular urban park in Oslo, Norway, known for its large green lawns, recreational areas, and role as a central gathering spot in the Grünerløkka district.
  • B. Fridegårdsparken
    Fridegårdsparken is a public park in the town of Enköping, Sweden, known for its landscaped green spaces and recreational areas.
  • C. Grøndalsparken
    Grøndalsparken is a public green space in the Copenhagen district of Vanløse, known for its recreational areas and walking paths along the Grøndal stream.
  • D. Bjørneparken
    Bjørneparken is a Norwegian wildlife and family adventure park known for its bears and other native animals, located in the municipality of Flå.
  • E. Rådhusparken
    Rådhusparken is a central public park in Sandvika, Norway, known as a local green space for recreation and community events.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0c47a95908190ae3e19b716accb3d completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f118ee5f1c8190b8c6c431039eb8c9 completed April 28, 2026, 8:30 p.m.
Created at: April 16, 2026, 7:05 p.m.