Triple

T10155372
Position Surface form Disambiguated ID Type / Status
Subject Flynas E232761 entity
Predicate loyaltyProgram P178 FINISHED
Object nasmiles
nasmiles is the frequent-flyer loyalty program of Saudi low-cost airline Flynas, offering members points and rewards for their travel and related spending.
 E843654 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: nasmiles | Statement: [Flynas, loyaltyProgram, nasmiles]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: nasmiles
Context triple: [Flynas, loyaltyProgram, nasmiles]
  • A. SMI
    SMI is the IATA airport code for Samos International Airport, the main air gateway to the Greek island of Samos.
  • B. SMI
    SMI is the Swiss Market Index, a leading stock market index that tracks the performance of major blue-chip companies listed on the SIX Swiss Exchange.
  • C. SMI
    SMI is the vehicle registration code assigned to the town of Mikołów in Poland.
  • D. SMO
    SMO is the IATA airport code for Santa Monica Airport, a general aviation facility located in Santa Monica, California.
  • E. Sm6
    Sm6 is a class of high-speed electric multiple unit trains used on the Allegro service between Finland and Russia.
  • 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: nasmiles
Triple: [Flynas, loyaltyProgram, nasmiles]
Generated description
nasmiles is the frequent-flyer loyalty program of Saudi low-cost airline Flynas, offering members points and rewards for their travel and related spending.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: nasmiles
Target entity description: nasmiles is the frequent-flyer loyalty program of Saudi low-cost airline Flynas, offering members points and rewards for their travel and related spending.
  • A. SMI
    SMI is the IATA airport code for Samos International Airport, the main air gateway to the Greek island of Samos.
  • B. SMI
    SMI is the vehicle registration code assigned to the town of Mikołów in Poland.
  • C. SMI
    SMI is the Swiss Market Index, a leading stock market index that tracks the performance of major blue-chip companies listed on the SIX Swiss Exchange.
  • D. SMO
    SMO is the IATA airport code for Santa Monica Airport, a general aviation facility located in Santa Monica, California.
  • E. Sm6
    Sm6 is a class of high-speed electric multiple unit trains used on the Allegro service between Finland and Russia.
  • 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_69ca84885e48819088a31b127cf44904 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cdec3a5e7c819098b2f9ccbde7cf94 completed April 2, 2026, 4:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2e6541e488190814e6395f219eb12 completed April 5, 2026, 10:46 p.m.
NEDg Description generation batch_69d2e7408e58819083c43e334a87a09f completed April 5, 2026, 10:50 p.m.
NED2 Entity disambiguation (via description) batch_69d2e7b854d08190ac2af642970b7f09 completed April 5, 2026, 10:52 p.m.
Created at: March 30, 2026, 9:09 p.m.