Triple

T4098463
Position Surface form Disambiguated ID Type / Status
Subject Helsinki Airport E87879 entity
Predicate focusCityFor P164 FINISHED
Object Norra
Norra is a Finnish regional airline that operates domestic and short-haul international flights, primarily on behalf of Finnair.
 E412709 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: Norra | Statement: [Helsinki Airport, focusCityFor, Norra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Norra
Context triple: [Helsinki Airport, focusCityFor, Norra]
  • A. Noord
    Noord is a major river in the western Netherlands that forms part of the Rhine–Meuse–Scheldt delta and serves as an important waterway for regional shipping and transport.
  • B. Nord
    Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
  • C. Nordlandet
    Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
  • D. Noorden
    Noorden is a village in the Dutch province of South Holland, known for its rural character and surrounding lakes and peatland nature reserves.
  • E. Ouest
    Ouest was the commonly used short name for the Compagnie des chemins de fer de l'Ouest, a major 19th-century French railway company serving the western regions of France.
  • 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: Norra
Triple: [Helsinki Airport, focusCityFor, Norra]
Generated description
Norra is a Finnish regional airline that operates domestic and short-haul international flights, primarily on behalf of Finnair.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Norra
Target entity description: Norra is a Finnish regional airline that operates domestic and short-haul international flights, primarily on behalf of Finnair.
  • A. Noord
    Noord is a major river in the western Netherlands that forms part of the Rhine–Meuse–Scheldt delta and serves as an important waterway for regional shipping and transport.
  • B. Nord
    Nord is a department in northern France known for its industrial heritage, dense population, and proximity to Belgium.
  • C. Nordlandet
    Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
  • D. Noorden
    Noorden is a village in the Dutch province of South Holland, known for its rural character and surrounding lakes and peatland nature reserves.
  • E. Ouest
    Ouest was the commonly used short name for the Compagnie des chemins de fer de l'Ouest, a major 19th-century French railway company serving the western regions of France.
  • 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_69aed94564cc8190a9c1457daedb6e7f completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefce017708190a4d33753fd32a7bb completed March 9, 2026, 5:01 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b725788819091c6aaeccfb86964 completed March 14, 2026, 2:06 p.m.
NEDg Description generation batch_69b56c5bb12881908cf2c74d68ecd5b6 completed March 14, 2026, 2:10 p.m.
NED2 Entity disambiguation (via description) batch_69b56cc5c704819083dac59bf7b3cb83 completed March 14, 2026, 2:12 p.m.
Created at: March 9, 2026, 3:40 p.m.