Triple

T6581089
Position Surface form Disambiguated ID Type / Status
Subject Norwegian language policy framework E157297 entity
Predicate regulates P46 FINISHED
Object Nynorsk E92855 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: Nynorsk | Statement: [Norwegian language policy framework, regulates, Nynorsk]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nynorsk
Context triple: [Norwegian language policy framework, regulates, Nynorsk]
  • A. Nynorsk chosen
    Nynorsk is one of the two official written standards of the Norwegian language, based primarily on rural and western Norwegian dialects.
  • B. Middle Norwegian
    Middle Norwegian is a historical North Germanic language stage spoken in Norway roughly between the late Middle Ages and the early modern period, bridging Old Norwegian and modern Norwegian.
  • C. New Norwegian
    New Norwegian is one of the two official written standards of the Norwegian language, developed in the 19th century from rural Norwegian dialects.
  • D. Norwegian language
    Norwegian is a North Germanic language spoken primarily in Norway, closely related to Danish and Swedish and featuring two official written standards, Bokmål and Nynorsk.
  • E. Bokmål
    Bokmål is the most widely used written standard of the Norwegian language, employed in government, education, media, and everyday communication.
  • 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_69c6882b3a108190b3a9eb343ae4162c completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6ae90c1b081908f851bff1dd19855 completed March 27, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6d572c4708190844f4b1abee8ca86 completed March 27, 2026, 7:07 p.m.
Created at: March 27, 2026, 1:54 p.m.