Triple

T7591553
Position Surface form Disambiguated ID Type / Status
Subject South Funen Archipelago E179746 entity
Predicate containsIsland P970 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: [South Funen Archipelago, containsIsland, Tåsinge]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tåsinge
Context triple: [South Funen Archipelago, containsIsland, 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_69c69f335248819093c1006f30513708 completed March 27, 2026, 3:16 p.m.
NER Named-entity recognition batch_69c6f9b746ac8190b255afdfb9635f72 completed March 27, 2026, 9:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c87080c19c8190ba6a632f6f277621 completed March 29, 2026, 12:21 a.m.
Created at: March 27, 2026, 3:53 p.m.