Triple

T4434665
Position Surface form Disambiguated ID Type / Status
Subject Buskerud E95618 entity
Predicate contains P35 FINISHED
Object Nesbyen
Nesbyen is a small town and municipality in southeastern Norway known for its inland valley setting, historic wooden buildings, and notably warm summer temperatures.
 E440168 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: Nesbyen | Statement: [Buskerud, contains, Nesbyen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nesbyen
Context triple: [Buskerud, contains, Nesbyen]
  • A. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • B. Trondenes
    Trondenes is a historic former municipality and parish in northern Norway, known for its medieval stone church and role as an administrative center in the Harstad region.
  • C. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • D. Bolnes
    Bolnes is a Dutch surname most notably associated with Catharina Bolnes, the wife of painter Johannes Vermeer.
  • E. Birkenes
    Birkenes is a rural municipality in Agder county in southern Norway, known for its forests, rivers, and small villages.
  • 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: Nesbyen
Triple: [Buskerud, contains, Nesbyen]
Generated description
Nesbyen is a small town and municipality in southeastern Norway known for its inland valley setting, historic wooden buildings, and notably warm summer temperatures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nesbyen
Target entity description: Nesbyen is a small town and municipality in southeastern Norway known for its inland valley setting, historic wooden buildings, and notably warm summer temperatures.
  • A. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • B. Trondenes
    Trondenes is a historic former municipality and parish in northern Norway, known for its medieval stone church and role as an administrative center in the Harstad region.
  • C. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • D. Bolnes
    Bolnes is a Dutch surname most notably associated with Catharina Bolnes, the wife of painter Johannes Vermeer.
  • E. Birkenes
    Birkenes is a rural municipality in Agder county in southern Norway, known for its forests, rivers, and small villages.
  • 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_69b3453ea2b48190a26f154b3b8fece5 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35588e99881908fea7b71a33e2bb6 completed March 13, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6137378dc8190900c8fda2693c4da completed March 15, 2026, 2:03 a.m.
NEDg Description generation batch_69b61439a86c8190849c5af718ddc647 completed March 15, 2026, 2:06 a.m.
NED2 Entity disambiguation (via description) batch_69b614d6106c81908a601f540622f934 completed March 15, 2026, 2:09 a.m.
Created at: March 12, 2026, 11:31 p.m.