Triple

T1354105
Position Surface form Disambiguated ID Type / Status
Subject Norwegian Air Shuttle E28947 entity
Predicate alliance P600 FINISHED
Object Norwegian Reward
Norwegian Reward is the frequent flyer loyalty program of low-cost airline Norwegian Air Shuttle, offering members points and travel-related benefits.
 E156092 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: Norwegian Reward | Statement: [Norwegian Air Shuttle, alliance, Norwegian Reward]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Norwegian Reward
Context triple: [Norwegian Air Shuttle, alliance, Norwegian Reward]
  • A. Solbo
    Solbo is a locality within Botkyrka Municipality in Stockholm County, Sweden.
  • B. Troms
    Troms was a former county in northern Norway known for its Arctic landscapes, coastal fjords, and the city of Tromsø.
  • C. Øyer
    Øyer is a small municipality in Innlandet county, Norway, known for its rural valley landscape and proximity to the Hafjell ski resort.
  • D. Kongsseteren
    Kongsseteren is a historic winter residence and retreat used by the Norwegian royal family near Oslo.
  • E. 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.
  • 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: Norwegian Reward
Triple: [Norwegian Air Shuttle, alliance, Norwegian Reward]
Generated description
Norwegian Reward is the frequent flyer loyalty program of low-cost airline Norwegian Air Shuttle, offering members points and travel-related benefits.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Norwegian Reward
Target entity description: Norwegian Reward is the frequent flyer loyalty program of low-cost airline Norwegian Air Shuttle, offering members points and travel-related benefits.
  • A. Solbo
    Solbo is a locality within Botkyrka Municipality in Stockholm County, Sweden.
  • B. Troms
    Troms was a former county in northern Norway known for its Arctic landscapes, coastal fjords, and the city of Tromsø.
  • C. Øyer
    Øyer is a small municipality in Innlandet county, Norway, known for its rural valley landscape and proximity to the Hafjell ski resort.
  • D. Kongsseteren
    Kongsseteren is a historic winter residence and retreat used by the Norwegian royal family near Oslo.
  • E. 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.
  • 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_69a498571d248190a0ac9eb02d97097f completed March 1, 2026, 7:49 p.m.
NER Named-entity recognition batch_69a4c26e916c8190b4b324df87f4c121 completed March 1, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69acce6bf6188190a0e1c8acd16b8088 completed March 8, 2026, 1:18 a.m.
NEDg Description generation batch_69accf47541481909f2032cdf8e4272b completed March 8, 2026, 1:22 a.m.
NED2 Entity disambiguation (via description) batch_69accff8af488190a7580cf7c02ceed9 completed March 8, 2026, 1:25 a.m.
Created at: March 1, 2026, 7:56 p.m.