Triple

T20798292
Position Surface form Disambiguated ID Type / Status
Subject Trønderbanen E511970 entity
Predicate hasStation P35 FINISHED
Object Lademoen
Lademoen is a neighborhood in Trondheim, Norway, known for its residential character and proximity to the city center.
 E1451254 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: Lademoen | Statement: [Trønderbanen, hasStation, Lademoen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lademoen
Context triple: [Trønderbanen, hasStation, Lademoen]
  • A. Gröndal
    Gröndal is a residential district in southern Stockholm, Sweden, known for its waterfront location on Lake Mälaren and mix of early 20th-century and modern architecture.
  • B. Laukvik
    Laukvik is a small coastal village in northern Norway, located in the Lofoten archipelago and known for its scenic Arctic landscapes and fishing heritage.
  • C. Laukvik
    Laukvik is a small coastal settlement located on the island of Rebbenesøya in northern Norway.
  • D. Lodalen
    Lodalen is a small valley and residential-industrial area in Oslo, Norway, situated near the inner-city districts and railway facilities.
  • E. 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.
  • 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: Lademoen
Triple: [Trønderbanen, hasStation, Lademoen]
Generated description
Lademoen is a neighborhood in Trondheim, Norway, known for its residential character and proximity to the city center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lademoen
Target entity description: Lademoen is a neighborhood in Trondheim, Norway, known for its residential character and proximity to the city center.
  • A. Gröndal
    Gröndal is a residential district in southern Stockholm, Sweden, known for its waterfront location on Lake Mälaren and mix of early 20th-century and modern architecture.
  • B. Laukvik
    Laukvik is a small coastal village in northern Norway, located in the Lofoten archipelago and known for its scenic Arctic landscapes and fishing heritage.
  • C. Laukvik
    Laukvik is a small coastal settlement located on the island of Rebbenesøya in northern Norway.
  • D. Lodalen
    Lodalen is a small valley and residential-industrial area in Oslo, Norway, situated near the inner-city districts and railway facilities.
  • E. 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.
  • 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_69e0b4cc69f481908e98751e697b9df4 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c2ae2c4c819087f620df31dc1aba completed April 21, 2026, 12:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a08f8c11b348190a15c80b400486e59 completed May 16, 2026, 11:07 p.m.
NEDg Description generation batch_6a08f9c9314c81909052d1a2e93d740a completed May 16, 2026, 11:12 p.m.
NED2 Entity disambiguation (via description) batch_6a08fa7040d88190b592d4f46249c2c1 completed May 16, 2026, 11:14 p.m.
Created at: April 16, 2026, 12:39 p.m.