Triple

T661408
Position Surface form Disambiguated ID Type / Status
Subject Paris Orly Airport E11762 entity
Predicate hasRunway P105 FINISHED
Object Runway 02/20
Runway 02/20 is one of the principal landing and takeoff runways at Paris Orly Airport, serving both domestic and international air traffic.
 E82678 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 02/20 | Statement: [Paris Orly Airport, hasRunway, Runway 02/20]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Runway 02/20
Context triple: [Paris Orly Airport, hasRunway, Runway 02/20]
  • A. Runway 04/22
    Runway 04/22 is one of LaGuardia Airport’s primary runways, used for handling a significant portion of the airport’s takeoff and landing traffic.
  • B. Runway 04L/22R
    Runway 04L/22R is one of the primary paved runways used for aircraft takeoffs and landings at Wuhan Tianhe International Airport in Wuhan, China.
  • C. Runway 05R/23L
    Runway 05R/23L is one of the main parallel runways at Manchester Airport in the United Kingdom, used for handling commercial air traffic.
  • D. Runway 05L/23R
    Runway 05L/23R is one of the main parallel runways at Manchester Airport, used for handling a high volume of commercial air traffic.
  • E. Runway 01L/19R
    Runway 01L/19R is one of the primary parallel runways at San Francisco International Airport, used for both arrivals and departures in varying wind conditions.
  • 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 02/20
Triple: [Paris Orly Airport, hasRunway, Runway 02/20]
Generated description
Runway 02/20 is one of the principal landing and takeoff runways at Paris Orly Airport, serving both domestic and international air traffic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Runway 02/20
Target entity description: Runway 02/20 is one of the principal landing and takeoff runways at Paris Orly Airport, serving both domestic and international air traffic.
  • A. Runway 04/22
    Runway 04/22 is one of LaGuardia Airport’s primary runways, used for handling a significant portion of the airport’s takeoff and landing traffic.
  • B. Runway 04L/22R
    Runway 04L/22R is one of the primary paved runways used for aircraft takeoffs and landings at Wuhan Tianhe International Airport in Wuhan, China.
  • C. Runway 05R/23L
    Runway 05R/23L is one of the main parallel runways at Manchester Airport in the United Kingdom, used for handling commercial air traffic.
  • D. Runway 05L/23R
    Runway 05L/23R is one of the main parallel runways at Manchester Airport, used for handling a high volume of commercial air traffic.
  • E. Runway 01L/19R
    Runway 01L/19R is one of the primary parallel runways at San Francisco International Airport, used for both arrivals and departures in varying wind conditions.
  • 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_69a4932862a0819098be659c814e4981 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49fa954988190841740a587ace466 completed March 1, 2026, 8:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5c3963c588190b7116f3f7aad2687 completed March 2, 2026, 5:06 p.m.
NEDg Description generation batch_69a5c47442188190b26b31f2901478a1 completed March 2, 2026, 5:10 p.m.
NED2 Entity disambiguation (via description) batch_69a5ce7f2b6881909f6c657f8b9c5347 completed March 2, 2026, 5:53 p.m.
Created at: March 1, 2026, 7:36 p.m.