Triple

T1094712
Position Surface form Disambiguated ID Type / Status
Subject Skaugum E24245 entity
Predicate municipality P852 FINISHED
Object Asker E125781 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: Asker | Statement: [Skaugum, municipality, Asker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Asker
Context triple: [Skaugum, municipality, Asker]
  • A. Asker chosen
    Asker is a municipality in Viken county, Norway, known for its coastal location near Oslo and its mix of residential areas, cultural sites, and natural landscapes.
  • B. Askim
    Askim is a town in southeastern Norway that serves as one of the locations for Østfold University College’s campuses.
  • C. Ullensaker
    Ullensaker is a municipality in Viken county, Norway, best known for hosting Oslo Airport, Gardermoen, the country’s main international airport.
  • D. Kongsvinger
    Kongsvinger is a town and municipality in Innlandet county, Norway, known for its historic fortress overlooking the Glomma River and its role as a regional center near the Swedish border.
  • E. Helleren
    Helleren is a small historic settlement in Norway known for its traditional houses built under a large rock overhang near the Jøssingfjord.
  • 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_69a4940542308190ac2a0b1f730b7cfc completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4b99e92308190b8a8c499e1630672 completed March 1, 2026, 10:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac538ee1ec8190b704bb8414fa0cef completed March 7, 2026, 4:34 p.m.
Created at: March 1, 2026, 7:42 p.m.