Triple

T18240668
Position Surface form Disambiguated ID Type / Status
Subject Nedre Eiker E436799 entity
Predicate locatedIn P40 FINISHED
Object Drammensregionen 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: Drammensregionen | Statement: [Nedre Eiker, locatedIn, Drammensregionen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Drammensregionen
Context triple: [Nedre Eiker, locatedIn, Drammensregionen]
  • A. Drammensregionen chosen
    Drammensregionen is a metropolitan area in southeastern Norway centered around the city of Drammen and its surrounding municipalities.
  • B. Bergen region
    The Bergen region is a coastal metropolitan area in western Norway centered on the city of Bergen and its surrounding islands and municipalities.
  • C. Gjøvik Region
    Gjøvik Region is a regional area in Innlandet county, Norway, centered around the town of Gjøvik and its surrounding municipalities.
  • D. Greater Oslo Region
    The Greater Oslo Region is the metropolitan area surrounding Norway’s capital, encompassing Oslo and its neighboring municipalities as a unified economic and commuter region.
  • E. Lillestrøm region
    The Lillestrøm region is an area in Norway centered around the town of Lillestrøm, known for its strong football culture and local support for clubs such as LSK Kvinner FK.
  • 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_69d8b91104e08190a8241f7d260a5162 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4f7e287548190b666a990e5b168b0 completed April 19, 2026, 3:42 p.m.
Created at: April 10, 2026, 10:33 a.m.