Triple

T15216779
Position Surface form Disambiguated ID Type / Status
Subject Sogn og Fjordane traditional district E363656 entity
Predicate containsPart P35 FINISHED
Object Sogn E1130035 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: Sogn | Statement: [Sogn og Fjordane traditional district, containsPart, Sogn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sogn
Context triple: [Sogn og Fjordane traditional district, containsPart, Sogn]
  • A. Sogn chosen
    Sogn is a traditional district in western Norway known for its dramatic fjord landscapes, including parts of the famous Sognefjord.
  • B. Strømsø
    Strømsø is a historic district and former separate town that now forms part of the city of Drammen in Norway.
  • C. Vendsyssel
    Vendsyssel is a region in northern Denmark forming the northernmost part of the Jutland peninsula, known for its coastal landscapes and rural towns.
  • D. Snogebæk
    Snogebæk is a small coastal village and fishing hamlet on the Danish island of Bornholm, known for its harbor, beaches, and holiday atmosphere.
  • E. 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.
  • 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_69d85a0ce24c81909c4d3b6475548c95 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0076f90c481909989befe031a2cae completed April 15, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5ebb8d48190b4afc540da8e6a4b completed May 9, 2026, 7:44 a.m.
Created at: April 10, 2026, 3:11 a.m.