Triple

T3145052
Position Surface form Disambiguated ID Type / Status
Subject Innlandet E65742 entity
Predicate containsPart P35 FINISHED
Object Gausdal
Gausdal is a rural municipality in southeastern Norway known for its agricultural landscape, forests, and outdoor recreational opportunities.
E359649 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: Gausdal | Statement: [Innlandet, containsPart, Gausdal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Gausdal
Context triple: [Innlandet, containsPart, Gausdal]
  • 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. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • C. Verdal
    Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
  • D. Gauldalen
    Gauldalen is a river valley and traditional district in central Norway known for its agricultural landscape and the Gaula River running through it.
  • E. Sokndal
    Sokndal is a coastal municipality in Rogaland county in southwestern Norway, known for its rugged coastline, historic settlements, and distinctive geological landscapes.
  • 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: Gausdal
Triple: [Innlandet, containsPart, Gausdal]
Generated description
Gausdal is a rural municipality in southeastern Norway known for its agricultural landscape, forests, and outdoor recreational opportunities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Gausdal
Target entity description: Gausdal is a rural municipality in southeastern Norway known for its agricultural landscape, forests, and outdoor recreational opportunities.
  • 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. Gaustad
    Gaustad is a district in Oslo, Norway, known for hosting major academic and research institutions, including parts of the University of Oslo campus.
  • C. Verdal
    Verdal is a municipality in central Norway known for its agricultural landscape, industrial activity, and the historic battlefield of Stiklestad.
  • D. Gauldalen
    Gauldalen is a river valley and traditional district in central Norway known for its agricultural landscape and the Gaula River running through it.
  • E. Sokndal
    Sokndal is a coastal municipality in Rogaland county in southwestern Norway, known for its rugged coastline, historic settlements, and distinctive geological landscapes.
  • 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_69ad8582f564819088c27e1f96153938 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada595d4548190b720a6131817833b completed March 8, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3608e261081908b0633bf11ea655b completed March 13, 2026, 12:55 a.m.
NEDg Description generation batch_69b3614702348190bd35c37d2059312f completed March 13, 2026, 12:58 a.m.
NED2 Entity disambiguation (via description) batch_69b362451b848190a2fe80a17ab8f9c3 completed March 13, 2026, 1:03 a.m.
Created at: March 8, 2026, 3:05 p.m.