Triple

T3701768
Position Surface form Disambiguated ID Type / Status
Subject Troms E80793 entity
Predicate containsTown P847 FINISHED
Object Evenskjer
Evenskjer is a small village in Northern Norway that serves as an administrative and service center in the Troms region.
 E413548 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: Evenskjer | Statement: [Troms, containsTown, Evenskjer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Evenskjer
Context triple: [Troms, containsTown, Evenskjer]
  • A. Mortensrud
    Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
  • B. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • C. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • D. Skøyen
    Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
  • E. Kjelsås
    Kjelsås is a residential neighborhood in northern Oslo, Norway, known for its hilly terrain, proximity to Marka forest, and access to the city via tram and rail connections.
  • 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: Evenskjer
Triple: [Troms, containsTown, Evenskjer]
Generated description
Evenskjer is a small village in Northern Norway that serves as an administrative and service center in the Troms region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Evenskjer
Target entity description: Evenskjer is a small village in Northern Norway that serves as an administrative and service center in the Troms region.
  • A. Mortensrud
    Mortensrud is a residential neighborhood in the Søndre Nordstrand borough of Oslo, Norway, known for its multicultural population and modern church, and served as the terminus of an Oslo Metro line.
  • B. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • C. Bekkestua
    Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
  • D. Skøyen
    Skøyen is a neighborhood in western Oslo, Norway, known as a busy residential and commercial hub with strong public transport connections.
  • E. Kjelsås
    Kjelsås is a residential neighborhood in northern Oslo, Norway, known for its hilly terrain, proximity to Marka forest, and access to the city via tram and rail connections.
  • 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_69ad8b1793888190a5f70e4b21dc05a1 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adc547c1848190a1ece46c59b7c43d completed March 8, 2026, 6:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b316140819089c90f3e2bd81ad8 completed March 14, 2026, 2:05 p.m.
NEDg Description generation batch_69b56f05a6d48190a5bf5b5279134dc8 completed March 14, 2026, 2:21 p.m.
NED2 Entity disambiguation (via description) batch_69b56f5253c88190baef7397d60aa300 completed March 14, 2026, 2:23 p.m.
Created at: March 8, 2026, 3:33 p.m.