Triple

T15305301
Position Surface form Disambiguated ID Type / Status
Subject Karasjok E365882 entity
Predicate bordersMunicipality P224 FINISHED
Object Kautokeino
Kautokeino is a large, sparsely populated municipality in Norway’s Finnmark region, known as a cultural center of the Sámi people and reindeer herding.
 E1150818 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: Kautokeino | Statement: [Karasjok, bordersMunicipality, Kautokeino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kautokeino
Context triple: [Karasjok, bordersMunicipality, Kautokeino]
  • A. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • B. Finnsnes
    Finnsnes is a small coastal town in northern Norway that serves as a commercial and transport hub for the island municipality of Senja.
  • C. Arvidsjaur
    Arvidsjaur is a small town in northern Sweden known for its military presence, winter testing facilities, and proximity to Arctic wilderness.
  • D. Follebu
    Follebu is a village in Innlandet county, Norway, known for its rural setting and traditional Norwegian countryside character within Gausdal municipality.
  • E. Snåsa
    Snåsa is a rural municipality in Trøndelag county, Norway, known for its large lakes, forests, and strong South Sámi cultural heritage.
  • 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: Kautokeino
Triple: [Karasjok, bordersMunicipality, Kautokeino]
Generated description
Kautokeino is a large, sparsely populated municipality in Norway’s Finnmark region, known as a cultural center of the Sámi people and reindeer herding.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kautokeino
Target entity description: Kautokeino is a large, sparsely populated municipality in Norway’s Finnmark region, known as a cultural center of the Sámi people and reindeer herding.
  • A. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • B. Finnsnes
    Finnsnes is a small coastal town in northern Norway that serves as a commercial and transport hub for the island municipality of Senja.
  • C. Arvidsjaur
    Arvidsjaur is a small town in northern Sweden known for its military presence, winter testing facilities, and proximity to Arctic wilderness.
  • D. Follebu
    Follebu is a village in Innlandet county, Norway, known for its rural setting and traditional Norwegian countryside character within Gausdal municipality.
  • E. Snåsa
    Snåsa is a rural municipality in Trøndelag county, Norway, known for its large lakes, forests, and strong South Sámi cultural heritage.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03ccef14c819099c5ebe962e7f867 completed April 16, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff01e47d8c8190844d45dda9a3e5ea completed May 9, 2026, 9:44 a.m.
NEDg Description generation batch_69ff02ec353881909c7b7a7ba60d0dbc completed May 9, 2026, 9:48 a.m.
NED2 Entity disambiguation (via description) batch_69ff035170248190a9aca4345996c661 completed May 9, 2026, 9:50 a.m.
Created at: April 10, 2026, 3:16 a.m.