Triple

T5098103
Position Surface form Disambiguated ID Type / Status
Subject Møre og Romsdal E114915 entity
Predicate containsSettlement P847 FINISHED
Object Gjemnes
Gjemnes is a rural municipality in western Norway known for its fjord landscapes and location between the towns of Molde and Kristiansund.
 E524907 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: Gjemnes | Statement: [Møre og Romsdal, containsSettlement, Gjemnes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gjemnes
Context triple: [Møre og Romsdal, containsSettlement, Gjemnes]
  • A. Gjesdal
    Gjesdal is a municipality in Rogaland county in southwestern Norway, known for its rural landscapes and proximity to the city of Stavanger.
  • B. Flesberg
    Flesberg is a rural municipality in southeastern Norway known for its forests, traditional wooden architecture, and location in the Numedal valley.
  • C. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • D. Engerdal
    Engerdal is a sparsely populated municipality in Innlandet county, Norway, known for its vast forests, lakes, and proximity to the Swedish border.
  • E. Fosnes
    Fosnes was a former rural municipality in Trøndelag county, Norway, known for its coastal landscape and small, dispersed population.
  • 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: Gjemnes
Triple: [Møre og Romsdal, containsSettlement, Gjemnes]
Generated description
Gjemnes is a rural municipality in western Norway known for its fjord landscapes and location between the towns of Molde and Kristiansund.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gjemnes
Target entity description: Gjemnes is a rural municipality in western Norway known for its fjord landscapes and location between the towns of Molde and Kristiansund.
  • A. Gjesdal
    Gjesdal is a municipality in Rogaland county in southwestern Norway, known for its rural landscapes and proximity to the city of Stavanger.
  • B. Flesberg
    Flesberg is a rural municipality in southeastern Norway known for its forests, traditional wooden architecture, and location in the Numedal valley.
  • C. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • D. Engerdal
    Engerdal is a sparsely populated municipality in Innlandet county, Norway, known for its vast forests, lakes, and proximity to the Swedish border.
  • E. Fosnes
    Fosnes was a former rural municipality in Trøndelag county, Norway, known for its coastal landscape and small, dispersed population.
  • 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_69bd443fc49c819089629c00e311310c completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7567d21081909227ed8f08b74c71 completed March 20, 2026, 4:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf7fcb783881909cc693e4832a19e3 completed March 22, 2026, 5:36 a.m.
NEDg Description generation batch_69bf804be0f881908f990e40478ff2d9 completed March 22, 2026, 5:38 a.m.
NED2 Entity disambiguation (via description) batch_69bf809da5288190a0f83f4b877eeaab completed March 22, 2026, 5:39 a.m.
Created at: March 20, 2026, 1:40 p.m.