Triple

T4434675
Position Surface form Disambiguated ID Type / Status
Subject Buskerud E95618 entity
Predicate contains P35 FINISHED
Object Nore og Uvdal
Nore og Uvdal is a mountainous rural municipality in southern Norway known for its vast natural landscapes, outdoor recreation, and traditional cultural heritage.
E474129 NE FINISHED

How this triple was built (4 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: Nore og Uvdal | Statement: [Buskerud, contains, Nore og Uvdal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nore og Uvdal
Context triple: [Buskerud, contains, Nore og Uvdal]
  • A. Verdal
    Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
  • B. Sokndal
    Sokndal is a coastal municipality in Rogaland county in southwestern Norway, known for its rugged coastline, historic settlements, and distinctive geological landscapes.
  • C. Sandnes
    Sandnes is a city in southwestern Norway, near Stavanger, known for its proximity to fjords and outdoor recreation areas.
  • D. Håvik
    Håvik is a small coastal village located in Karmøy municipality in Rogaland county, southwestern Norway.
  • E. Sogndal
    Sogndal is a village and municipality in Vestland county, Norway, known for its scenic fjord landscape, agriculture, and as a regional education and service center.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nore og Uvdal
Triple: [Buskerud, contains, Nore og Uvdal]
Generated description
Nore og Uvdal is a mountainous rural municipality in southern Norway known for its vast natural landscapes, outdoor recreation, and traditional cultural heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Nore og Uvdal
Target entity description: Nore og Uvdal is a mountainous rural municipality in southern Norway known for its vast natural landscapes, outdoor recreation, and traditional cultural heritage.
  • A. Verdal
    Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
  • B. Sokndal
    Sokndal is a coastal municipality in Rogaland county in southwestern Norway, known for its rugged coastline, historic settlements, and distinctive geological landscapes.
  • C. Sandnes
    Sandnes is a city in southwestern Norway, near Stavanger, known for its proximity to fjords and outdoor recreation areas.
  • D. Håvik
    Håvik is a small coastal village located in Karmøy municipality in Rogaland county, southwestern Norway.
  • E. Sogndal
    Sogndal is a village and municipality in Vestland county, Norway, known for its scenic fjord landscape, agriculture, and as a regional education and service center.
  • F. None of above. chosen

Provenance (5 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_69b3453ea2b48190a26f154b3b8fece5 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35588e99881908fea7b71a33e2bb6 completed March 13, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69be5c70e6cc8190b447d3bd1f578aae completed March 21, 2026, 8:53 a.m.
NEDg Description generation batch_69be5eb7a20881909177feb6e969af6c completed March 21, 2026, 9:02 a.m.
NED2 Entity disambiguation (via description) batch_69be5f1646588190a8d81316fce477db completed March 21, 2026, 9:04 a.m.
Created at: March 12, 2026, 11:31 p.m.