Triple

T21954054
Position Surface form Disambiguated ID Type / Status
Subject Asker Fotball E542137 entity
Predicate regionServed P82 FINISHED
Object Asker region 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: Asker region | Statement: [Asker Fotball, regionServed, Asker region]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Asker region
Context triple: [Asker Fotball, regionServed, Asker region]
  • A. Asker region chosen
    Asker region is a municipality and suburban area in Viken county, Norway, known for its coastal landscape along the Oslofjord and its role as part of the Greater Oslo region.
  • B. Gjøvik Region
    Gjøvik Region is a regional area in Innlandet county, Norway, centered around the town of Gjøvik and its surrounding municipalities.
  • C. Drammensregionen
    Drammensregionen is a metropolitan area in southeastern Norway centered around the city of Drammen and its surrounding municipalities.
  • D. Akershus county
    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.
  • E. Hadeland district
    Hadeland district is a traditional rural region in southeastern Norway known for its historic farms, forests, and lakes north of Oslo.
  • 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_69e0c47ef0e48190a50e1bcc43f4b3fd completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1243dfb4081909bc7a722843ffea7 completed April 28, 2026, 9:18 p.m.
Created at: April 16, 2026, 7:59 p.m.