Triple

T7274307
Position Surface form Disambiguated ID Type / Status
Subject Málaga Airport E162985 entity
Predicate hasRunway P105 FINISHED
Object Runway 12/30
Runway 12/30 is a principal paved runway at Málaga Airport in southern Spain, used for handling a large volume of commercial air traffic.
 E691996 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: Runway 12/30 | Statement: [Málaga Airport, hasRunway, Runway 12/30]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Runway 12/30
Context triple: [Málaga Airport, hasRunway, Runway 12/30]
  • A. Runway 12/30
    Runway 12/30 is the primary paved runway used for commercial flight operations at L.F. Wade International Airport in Bermuda.
  • B. Runway 12/30
    Runway 12/30 is a primary paved runway at San Carlos Airport in California, used mainly for general aviation operations.
  • C. Runway 12/30
    Runway 12/30 is a primary paved runway at Albuquerque International Sunport used for commercial and general aviation takeoffs and landings.
  • D. Runway 12/30
    Runway 12/30 is a principal runway at Cairns Airport in Queensland, Australia, used for both domestic and international aircraft operations.
  • E. Runway 12/30
    Runway 12/30 is a primary paved runway at Edmonton International Airport used for handling a wide range of commercial and general aviation traffic.
  • 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: Runway 12/30
Triple: [Málaga Airport, hasRunway, Runway 12/30]
Generated description
Runway 12/30 is a principal paved runway at Málaga Airport in southern Spain, used for handling a large volume of commercial air traffic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Runway 12/30
Target entity description: Runway 12/30 is a principal paved runway at Málaga Airport in southern Spain, used for handling a large volume of commercial air traffic.
  • A. Runway 12/30
    Runway 12/30 is a primary paved runway at Albuquerque International Sunport used for commercial and general aviation takeoffs and landings.
  • B. Runway 12/30
    Runway 12/30 is a primary paved runway at Edmonton International Airport used for handling a wide range of commercial and general aviation traffic.
  • C. Runway 12/30
    Runway 12/30 is a principal paved runway at Canberra Airport used for handling domestic and international air traffic.
  • D. Runway 12/30
    Runway 12/30 is a primary paved runway at San Carlos Airport in California, used mainly for general aviation operations.
  • E. Runway 12/30
    Runway 12/30 is a principal paved runway at Pointe-à-Pitre International Airport in Guadeloupe, used for handling both domestic and international air traffic.
  • 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_69c6885c5964819085b209701769877f completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6eb0de9f48190807dd148758bad62 completed March 27, 2026, 8:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69c9cd7f9b2c81908a1f77a9cc37a0be completed March 30, 2026, 1:10 a.m.
NEDg Description generation batch_69c9ce2791d88190bc48f237e134e7a9 completed March 30, 2026, 1:13 a.m.
NED2 Entity disambiguation (via description) batch_69c9ce745fdc8190843c4d48722fb263 completed March 30, 2026, 1:14 a.m.
Created at: March 27, 2026, 2:58 p.m.