Triple

T8309557
Position Surface form Disambiguated ID Type / Status
Subject Nøtterøy E194555 entity
Predicate neighboringIsland P19482 FINISHED
Object Tjøme E190849 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: Tjøme | Statement: [Nøtterøy, neighboringIsland, Tjøme]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tjøme
Context triple: [Nøtterøy, neighboringIsland, Tjøme]
  • A. Tjøme chosen
    Tjøme is a scenic island and former municipality in Vestfold, Norway, known for its coastal landscapes, summer cabins, and popular seaside recreation areas.
  • B. Strømsø
    Strømsø is a historic district and former separate town that now forms part of the city of Drammen in Norway.
  • C. Strynø
    Strynø is a small Danish island in the Baltic Sea known for its rural charm, traditional village environment, and location between the larger islands of Langeland and Ærø.
  • D. Drøbak
    Drøbak is a coastal town in southeastern Norway known for its historic harbor, charming wooden houses, and role as a gateway to the Oslofjord.
  • E. Randesund
    Randesund is a coastal district of Kristiansand in southern Norway, known for its scenic archipelago, beaches, and recreational outdoor areas.
  • 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_69ca82e613e88190bf8139669bbd0d53 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7f2d2c30819095075940479b75a7 completed March 31, 2026, 8 a.m.
NED1 Entity disambiguation (via context triple) batch_69cdc6e4eb808190b138c52810f35040 completed April 2, 2026, 1:31 a.m.
Created at: March 30, 2026, 5:54 p.m.