Triple

T1047265
Position Surface form Disambiguated ID Type / Status
Subject Oslo Central Station E22609 entity
Predicate servedBy P82 FINISHED
Object Flytoget
Flytoget is Norway’s high-speed airport express train service that connects Oslo Airport with Oslo and surrounding areas.
 E119736 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: Flytoget | Statement: [Oslo Central Station, servedBy, Flytoget]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Flytoget
Context triple: [Oslo Central Station, servedBy, Flytoget]
  • A. Vrbo
    Vrbo is a vacation rental marketplace that connects travelers with owners and property managers offering homes, condos, cabins, and other short-term lodging options worldwide.
  • B. Villeta
    Villeta is a Colombian town and municipality in the department of Cundinamarca, known for its warm climate and sugarcane production.
  • C. Lot
    Lot is a river in southwestern France known for flowing through scenic valleys and historic towns before joining the Garonne.
  • D. Lot
    Lot is a department in southwestern France known for its picturesque river valleys, medieval villages, and prehistoric cave art.
  • E. Venda
    Venda is a Bantu language of the Venda people of South Africa and Zimbabwe, recognized as one of South Africa’s official languages.
  • 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: Flytoget
Triple: [Oslo Central Station, servedBy, Flytoget]
Generated description
Flytoget is Norway’s high-speed airport express train service that connects Oslo Airport with Oslo and surrounding areas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Flytoget
Target entity description: Flytoget is Norway’s high-speed airport express train service that connects Oslo Airport with Oslo and surrounding areas.
  • A. Vrbo
    Vrbo is a vacation rental marketplace that connects travelers with owners and property managers offering homes, condos, cabins, and other short-term lodging options worldwide.
  • B. Villeta
    Villeta is a Colombian town and municipality in the department of Cundinamarca, known for its warm climate and sugarcane production.
  • C. Lot
    Lot is a river in southwestern France known for flowing through scenic valleys and historic towns before joining the Garonne.
  • D. Lot
    Lot is a department in southwestern France known for its picturesque river valleys, medieval villages, and prehistoric cave art.
  • E. Venda
    Venda is a Bantu language of the Venda people of South Africa and Zimbabwe, recognized as one of South Africa’s official languages.
  • 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_69a493da02e081908c13ff5e02a0fe7a completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b84d30888190b66f7245d781957d completed March 1, 2026, 10:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac3bcb65d08190b2ea04b6de3bb39b completed March 7, 2026, 2:52 p.m.
NEDg Description generation batch_69ac3ce6228881908f429cb0a016a17a completed March 7, 2026, 2:57 p.m.
NED2 Entity disambiguation (via description) batch_69ac3d3ed140819087ede15c555e2f4d completed March 7, 2026, 2:59 p.m.
Created at: March 1, 2026, 7:42 p.m.