Triple

T4206651
Position Surface form Disambiguated ID Type / Status
Subject Ringerike E93797 entity
Predicate borderedBy P224 FINISHED
Object Krødsherad
Krødsherad is a rural municipality in Buskerud, Norway, known for its scenic landscapes around Lake Krøderen and outdoor recreational opportunities.
 E422312 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: Krødsherad | Statement: [Ringerike, borderedBy, Krødsherad]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Krødsherad
Context triple: [Ringerike, borderedBy, Krødsherad]
  • A. Nordingrå
    Nordingrå is a small locality in Sweden’s High Coast region, known for its coastal landscapes and traditional rural communities.
  • B. Kragerø
    Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
  • C. Solør
    Solør is a traditional district in Eastern Norway known for its rural landscapes, forestry, and agriculture.
  • D. Ringerike
    Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
  • E. Kristinestad
    Kristinestad is a small coastal town in western Finland known for its well-preserved wooden old town and historic maritime character.
  • 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: Krødsherad
Triple: [Ringerike, borderedBy, Krødsherad]
Generated description
Krødsherad is a rural municipality in Buskerud, Norway, known for its scenic landscapes around Lake Krøderen and outdoor recreational opportunities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Krødsherad
Target entity description: Krødsherad is a rural municipality in Buskerud, Norway, known for its scenic landscapes around Lake Krøderen and outdoor recreational opportunities.
  • A. Nordingrå
    Nordingrå is a small locality in Sweden’s High Coast region, known for its coastal landscapes and traditional rural communities.
  • B. Kragerø
    Kragerø is a coastal town in Norway renowned for its picturesque archipelago, historic wooden buildings, and role as a popular summer holiday destination.
  • C. Solør
    Solør is a traditional district in Eastern Norway known for its rural landscapes, forestry, and agriculture.
  • D. Ringerike
    Ringerike is a historic district and municipality in southeastern Norway known for its rich Viking-age heritage and distinctive cultural traditions.
  • E. Kristinestad
    Kristinestad is a small coastal town in western Finland known for its well-preserved wooden old town and historic maritime character.
  • 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_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_69b596258db88190aed602eeb2323fee completed March 14, 2026, 5:08 p.m.
NEDg Description generation batch_69b59695c99481909a061751eaccbb25 completed March 14, 2026, 5:10 p.m.
NED2 Entity disambiguation (via description) batch_69b59a568e288190a87ba03b181f27df completed March 14, 2026, 5:26 p.m.
Created at: March 12, 2026, 11:03 p.m.