Triple

T4206610
Position Surface form Disambiguated ID Type / Status
Subject Ringerike E93797 entity
Predicate formerPartOf P5057 FINISHED
Object Buskerud county E95618 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: Buskerud county | Statement: [Ringerike, formerPartOf, Buskerud county]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Buskerud county
Context triple: [Ringerike, formerPartOf, Buskerud county]
  • A. Buskerud chosen
    Buskerud is a former county in southeastern Norway known for its varied landscape of forests, rivers, and mountains, including parts of the Hallingdal valley and Hardangervidda plateau.
  • B. Hedmark
    Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
  • C. Hedmarken
    Hedmarken is a traditional district in Innlandet county in eastern Norway, known for its agricultural landscapes and its central town, Hamar.
  • 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. 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.
  • 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_69b3451743608190808f41d17ccf2650 completed March 12, 2026, 10:58 p.m.
NER Named-entity recognition batch_69b3480cfacc81909a2705eb4e9ce8c1 completed March 12, 2026, 11:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69bde03b1f508190b9d5026103d3ee79 completed March 21, 2026, 12:03 a.m.
Created at: March 12, 2026, 11:03 p.m.