Triple

T3701785
Position Surface form Disambiguated ID Type / Status
Subject Troms E80793 entity
Predicate containsFjord P5879 FINISHED
Object Kvænangen
Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
 E383471 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: Kvænangen | Statement: [Troms, containsFjord, Kvænangen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Kvænangen
Context triple: [Troms, containsFjord, Kvænangen]
  • A. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • B. Kongsvinger
    Kongsvinger is a town and municipality in Innlandet county, Norway, known for its historic fortress overlooking the Glomma River and its role as a regional center near the Swedish border.
  • C. Haugalandet
    Haugalandet is a coastal region in western Norway centered around the town of Haugesund, known for its maritime heritage and North Sea industries.
  • D. Fagernes
    Fagernes is a small town in central Norway that serves as a regional hub and gateway to the mountainous Valdres district.
  • E. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • 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: Kvænangen
Triple: [Troms, containsFjord, Kvænangen]
Generated description
Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Kvænangen
Target entity description: Kvænangen is a fjord in northern Norway known for its dramatic coastal scenery, rich marine life, and traditional fishing communities.
  • A. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • B. Kongsvinger
    Kongsvinger is a town and municipality in Innlandet county, Norway, known for its historic fortress overlooking the Glomma River and its role as a regional center near the Swedish border.
  • C. Haugalandet
    Haugalandet is a coastal region in western Norway centered around the town of Haugesund, known for its maritime heritage and North Sea industries.
  • D. Fagernes
    Fagernes is a small town in central Norway that serves as a regional hub and gateway to the mountainous Valdres district.
  • E. Bremsnes
    Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
  • 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_69b4db041370819084b04e46d95ca966 completed March 14, 2026, 3:50 a.m.
NEDg Description generation batch_69b4dbbc23f08190a03ef4e4197398a4 completed March 14, 2026, 3:53 a.m.
NED2 Entity disambiguation (via description) batch_69b4dcac6fdc81908998415ffe1aabaa completed March 14, 2026, 3:57 a.m.
Created at: March 8, 2026, 3:33 p.m.