Triple

T6724052
Position Surface form Disambiguated ID Type / Status
Subject VLG E153467 entity
Predicate airlineCallsign P13478 FINISHED
Object VUELING
VUELING is a Spanish low-cost airline based in Barcelona that operates extensive domestic and European routes.
E613615 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: VUELING | Statement: [VLG, airlineCallsign, VUELING]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: VUELING
Context triple: [VLG, airlineCallsign, VUELING]
  • A. Equair
    Equair is an Ecuadorian airline that operated domestic passenger flights, notably serving routes from Guayaquil and Quito.
  • B. Cargojet
    Cargojet is a Canadian cargo airline specializing in time-sensitive overnight air freight services across North America and select international routes.
  • C. Avion
    Avion is a commune in the Pas-de-Calais department in northern France.
  • D. Solaseed Air
    Solaseed Air is a Japanese regional airline based in Miyazaki that primarily operates domestic routes across Kyushu and other parts of Japan.
  • E. Aeroplan
    Aeroplan is Air Canada's loyalty program that allows members to earn and redeem points for flights, upgrades, and other travel-related rewards.
  • 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: VUELING
Triple: [VLG, airlineCallsign, VUELING]
Generated description
VUELING is a Spanish low-cost airline based in Barcelona that operates extensive domestic and European routes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: VUELING
Target entity description: VUELING is a Spanish low-cost airline based in Barcelona that operates extensive domestic and European routes.
  • A. Equair
    Equair is an Ecuadorian airline that operated domestic passenger flights, notably serving routes from Guayaquil and Quito.
  • B. Cargojet
    Cargojet is a Canadian cargo airline specializing in time-sensitive overnight air freight services across North America and select international routes.
  • C. Avion
    Avion is a commune in the Pas-de-Calais department in northern France.
  • D. Solaseed Air
    Solaseed Air is a Japanese regional airline based in Miyazaki that primarily operates domestic routes across Kyushu and other parts of Japan.
  • E. Aeroplan
    Aeroplan is Air Canada's loyalty program that allows members to earn and redeem points for flights, upgrades, and other travel-related rewards.
  • 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_69c6880afb988190ad88011b48ecfcba completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d13b296c8190bf54009063032c6d completed March 27, 2026, 6:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69c700a34e248190ba9d73437b19a96d completed March 27, 2026, 10:11 p.m.
NEDg Description generation batch_69c703ad7e0c81908da32c96806f3b07 completed March 27, 2026, 10:24 p.m.
NED2 Entity disambiguation (via description) batch_69c7042f23408190b06faafcb3251276 completed March 27, 2026, 10:26 p.m.
Created at: March 27, 2026, 2:08 p.m.