Triple

T5237967
Position Surface form Disambiguated ID Type / Status
Subject Cancún International Airport E118270 entity
Predicate IATAcode P418 FINISHED
Object CUN
CUN is the IATA airport code for Cancún International Airport, a major gateway for international tourism to Mexico’s Caribbean coast.
 E504548 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: CUN | Statement: [Cancún International Airport, IATAcode, CUN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: CUN
Context triple: [Cancún International Airport, IATAcode, CUN]
  • A. CUL
    CUL is the main research library of the University of Cambridge and one of the largest and most important academic libraries in the United Kingdom.
  • B. CU
    CU is the common abbreviation for the Christian Union, a Christian student organization found at many universities.
  • C. CU
    CU is the common abbreviation for Chulalongkorn University, a leading public research university in Bangkok, Thailand.
  • D. CU
    CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
  • E. WUN
    WUN is the vehicle registration code for the district of Wunsiedel im Fichtelgebirge in Upper Franconia, Germany.
  • 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: CUN
Triple: [Cancún International Airport, IATAcode, CUN]
Generated description
CUN is the IATA airport code for Cancún International Airport, a major gateway for international tourism to Mexico’s Caribbean coast.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: CUN
Target entity description: CUN is the IATA airport code for Cancún International Airport, a major gateway for international tourism to Mexico’s Caribbean coast.
  • A. CUL
    CUL is the main research library of the University of Cambridge and one of the largest and most important academic libraries in the United Kingdom.
  • B. CU
    CU is the common abbreviation for Chulalongkorn University, a leading public research university in Bangkok, Thailand.
  • C. CU
    CU is the common abbreviation for the Christian Union, a Christian student organization found at many universities.
  • D. CU
    CU is the two-letter ISO 3166-1 alpha-2 country code assigned to Cuba.
  • E. WUN
    WUN is the vehicle registration code for the district of Wunsiedel im Fichtelgebirge in Upper Franconia, Germany.
  • 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_69bd4467db0881909b3b0982df32cc8f completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7b27990c8190b6a3c24de09c8c18 completed March 20, 2026, 4:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69bef820285881909f0e569e020a58ae completed March 21, 2026, 7:57 p.m.
NEDg Description generation batch_69bef8acecf48190a1d3f56640bf7784 completed March 21, 2026, 7:59 p.m.
NED2 Entity disambiguation (via description) batch_69bef92017948190906d1be3551b54c2 completed March 21, 2026, 8:01 p.m.
Created at: March 20, 2026, 1:49 p.m.