Triple

T5838096
Position Surface form Disambiguated ID Type / Status
Subject Kjeller campus E129522 entity
Predicate regionServed P82 FINISHED
Object Akershus region E246411 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: Akershus region | Statement: [Kjeller campus, regionServed, Akershus region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Akershus region
Context triple: [Kjeller campus, regionServed, Akershus region]
  • A. Drammensregionen
    Drammensregionen is a metropolitan area in southeastern Norway centered around the city of Drammen and its surrounding municipalities.
  • B. Akershus county chosen
    Akershus county was a former county in southeastern Norway that historically surrounded Oslo and included both urban suburbs and rural areas before being merged into Viken county.
  • C. Oslo county
    Oslo county is Norway’s capital county, encompassing the city of Oslo and serving as the country’s political, economic, and cultural center.
  • D. Gjøvik Region
    Gjøvik Region is a regional area in Innlandet county, Norway, centered around the town of Gjøvik and its surrounding municipalities.
  • E. Hedmarken
    Hedmarken is a traditional district in Innlandet county in eastern Norway, known for its agricultural landscapes and its central town, Hamar.
  • 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_69c0084af79c81908af128ccc29983d0 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c034a66c448190a6ea7f9827cbffe9 completed March 22, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69c673ea34ec81909b2391a690ed502e completed March 27, 2026, 12:11 p.m.
Created at: March 22, 2026, 3:54 p.m.