Triple

T7853778
Position Surface form Disambiguated ID Type / Status
Subject Skedsmo E182120 entity
Predicate hasMajorSettlement P316 FINISHED
Object Strømmen
Strømmen is a town in Lillestrøm municipality in Viken county, Norway, known for its shopping mall Strømmen Storsenter and its historical industrial and railway heritage.
E695704 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: Strømmen | Statement: [Skedsmo, hasMajorSettlement, Strømmen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Strømmen
Context triple: [Skedsmo, hasMajorSettlement, Strømmen]
  • A. Strond
    Strond is a small coastal village on the island of Borðoy in the Faroe Islands.
  • B. Støre
    Støre is a Norwegian surname most prominently associated with Jonas Gahr Støre, the Prime Minister of Norway and leader of the Labour Party.
  • C. Strømsø
    Strømsø is a historic district and former separate town that now forms part of the city of Drammen in Norway.
  • D. Børselva
    Børselva is a river in northern Norway known for flowing into Porsangerfjorden and for its scenic Arctic landscape and salmon fishing.
  • E. Krokstadelva
    Krokstadelva is a town in Viken county, Norway, situated along the Drammenselva river and known historically for its industrial and residential character.
  • 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: Strømmen
Triple: [Skedsmo, hasMajorSettlement, Strømmen]
Generated description
Strømmen is a town in Lillestrøm municipality in Viken county, Norway, known for its shopping mall Strømmen Storsenter and its historical industrial and railway heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Strømmen
Target entity description: Strømmen is a town in Lillestrøm municipality in Viken county, Norway, known for its shopping mall Strømmen Storsenter and its historical industrial and railway heritage.
  • A. Strond
    Strond is a small coastal village on the island of Borðoy in the Faroe Islands.
  • B. Støre
    Støre is a Norwegian surname most prominently associated with Jonas Gahr Støre, the Prime Minister of Norway and leader of the Labour Party.
  • C. Strømsø
    Strømsø is a historic district and former separate town that now forms part of the city of Drammen in Norway.
  • D. Børselva
    Børselva is a river in northern Norway known for flowing into Porsangerfjorden and for its scenic Arctic landscape and salmon fishing.
  • E. Krokstadelva
    Krokstadelva is a town in Viken county, Norway, situated along the Drammenselva river and known historically for its industrial and residential character.
  • 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_69ca82869ee08190b8f9040dbc2c0467 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb18ed56d481909266d862e0ae152d completed March 31, 2026, 12:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5b1e9e808190a0eb2dea5288e743 completed March 31, 2026, 5:26 a.m.
NEDg Description generation batch_69cb5def38e88190864d84abd7959aa3 completed March 31, 2026, 5:38 a.m.
NED2 Entity disambiguation (via description) batch_69cb767b198481909cfc1f7a44e6f0d8 completed March 31, 2026, 7:23 a.m.
Created at: March 30, 2026, 4:51 p.m.