Triple

T9603116
Position Surface form Disambiguated ID Type / Status
Subject Svendborg E231898 entity
Predicate locatedNear P294 FINISHED
Object Tåsinge E675341 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: Tåsinge | Statement: [Svendborg, locatedNear, Tåsinge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tåsinge
Context triple: [Svendborg, locatedNear, Tåsinge]
  • A. Tåsinge chosen
    Tåsinge is a Danish island in the South Funen Archipelago known for its picturesque villages, coastal landscapes, and historic manor houses.
  • B. Abildsø
    Abildsø is a residential neighborhood in the borough of Østensjø in Oslo, Norway, known for its green areas and proximity to the lake Østensjøvannet.
  • C. Vækerø
    Vækerø is a residential and commercial area in Oslo, Norway, located along the western waterfront and known for its mix of housing, offices, and green spaces.
  • D. Norderhov
    Norderhov is a village in the municipality of Ringerike in Buskerud, Norway, known for its historic church and rural surroundings.
  • E. 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.
  • 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_69ca8484838c8190b2049199d22fef70 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9a5af8f0819089408ed630afa812 completed April 1, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1792ba9388190b98d4fb081510c30 completed April 4, 2026, 8:48 p.m.
Created at: March 30, 2026, 8:08 p.m.