Triple

T2322095
Position Surface form Disambiguated ID Type / Status
Subject Viva Aerobus E48204 entity
Predicate callsign P1565 FINISHED
Object AEROENLACES
AEROENLACES is the radio callsign used by the Mexican low-cost airline Viva Aerobus during air traffic communications.
 E256641 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: AEROENLACES | Statement: [Viva Aerobus, callsign, AEROENLACES]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: AEROENLACES
Context triple: [Viva Aerobus, callsign, AEROENLACES]
  • A. LAN Airlines
    LAN Airlines was a major Chilean airline that became one of Latin America’s largest carriers before merging into the LATAM Airlines Group.
  • B. ATA Airlines
    ATA Airlines is an Iranian airline that operates domestic and regional flights, using Mehrabad International Airport in Tehran as one of its main bases.
  • C. EuroAtlantic Airways
    EuroAtlantic Airways is a Portuguese charter airline known for operating long-haul and wet-lease services for other carriers worldwide.
  • D. Aena
    Aena is the Spanish state-owned company that manages and operates the majority of airports in Spain and is one of the world’s largest airport operators by passenger traffic.
  • E. Volaris
    Volaris is a Mexican low-cost airline that operates domestic and international flights, primarily serving routes across Mexico, the United States, and Central America.
  • 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: AEROENLACES
Triple: [Viva Aerobus, callsign, AEROENLACES]
Generated description
AEROENLACES is the radio callsign used by the Mexican low-cost airline Viva Aerobus during air traffic communications.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: AEROENLACES
Target entity description: AEROENLACES is the radio callsign used by the Mexican low-cost airline Viva Aerobus during air traffic communications.
  • A. LAN Airlines
    LAN Airlines was a major Chilean airline that became one of Latin America’s largest carriers before merging into the LATAM Airlines Group.
  • B. ATA Airlines
    ATA Airlines is an Iranian airline that operates domestic and regional flights, using Mehrabad International Airport in Tehran as one of its main bases.
  • C. EuroAtlantic Airways
    EuroAtlantic Airways is a Portuguese charter airline known for operating long-haul and wet-lease services for other carriers worldwide.
  • D. Aena
    Aena is the Spanish state-owned company that manages and operates the majority of airports in Spain and is one of the world’s largest airport operators by passenger traffic.
  • E. Volaris
    Volaris is a Mexican low-cost airline that operates domestic and international flights, primarily serving routes across Mexico, the United States, and Central America.
  • 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_69a88aa308a88190b0b86c011fda7fce completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc6337e948190bb4860f7045914e1 completed March 7, 2026, 6:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae896b357c8190a6cdf99d5292037e completed March 9, 2026, 8:48 a.m.
NEDg Description generation batch_69ae8e8263a08190a0950dbb1336df70 completed March 9, 2026, 9:10 a.m.
NED2 Entity disambiguation (via description) batch_69ae8f9fe79c819080062587aed27379 completed March 9, 2026, 9:15 a.m.
Created at: March 4, 2026, 7:49 p.m.