Triple

T8172225
Position Surface form Disambiguated ID Type / Status
Subject Tjøme E190849 entity
Predicate countyBeforeReform P34485 FINISHED
Object Vestfold
Vestfold was a former county in southeastern Norway along the Oslofjord, known for its coastal towns, maritime heritage, and Viking history.
 E94296 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: Vestfold | Statement: [Tjøme, countyBeforeReform, Vestfold]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vestfold
Context triple: [Tjøme, countyBeforeReform, Vestfold]
  • A. Agder
    Agder is a county in southern Norway known for its long coastline, maritime heritage, and popular coastal towns and islands.
  • B. Vestfold og Telemark
    Vestfold og Telemark is a former county in southeastern Norway known for its coastal towns, industrial heritage, and varied landscapes from fjords to inland forests and mountains.
  • C. Aust-Agder
    Aust-Agder was a former county in southern Norway known for its coastal towns, forests, and role in the country’s maritime and timber industries.
  • D. Sogn og Fjordane
    Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
  • E. Hordaland
    Hordaland was a former county in western Norway known for its fjords, coastal landscapes, and the city of Bergen.
  • 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: Vestfold
Triple: [Tjøme, countyBeforeReform, Vestfold]
Generated description
Vestfold was a former county in southeastern Norway along the Oslofjord, known for its coastal towns, maritime heritage, and Viking history.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vestfold
Target entity description: Vestfold was a former county in southeastern Norway along the Oslofjord, known for its coastal towns, maritime heritage, and Viking history.
  • A. Agder
    Agder is a county in southern Norway known for its long coastline, maritime heritage, and popular coastal towns and islands.
  • B. Vestfold og Telemark chosen
    Vestfold og Telemark is a former county in southeastern Norway known for its coastal towns, industrial heritage, and varied landscapes from fjords to inland forests and mountains.
  • C. Aust-Agder
    Aust-Agder was a former county in southern Norway known for its coastal towns, forests, and role in the country’s maritime and timber industries.
  • D. Sogn og Fjordane
    Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
  • E. Hordaland
    Hordaland was a former county in western Norway known for its fjords, coastal landscapes, and the city of Bergen.
  • F. None of above.

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_69ca82c1c0a08190bf8692b4d91a03ca completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb4807c9808190ad91a9c688a4c7fd completed March 31, 2026, 4:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce022b87bc8190b390adf9170eb886 completed April 2, 2026, 5:44 a.m.
NEDg Description generation batch_69ce064211e48190b558d4355be659ba completed April 2, 2026, 6:01 a.m.
NED2 Entity disambiguation (via description) batch_69ce07a390048190ac26a7e3d3d561e0 completed April 2, 2026, 6:07 a.m.
Created at: March 30, 2026, 5:39 p.m.