Triple

T12664290
Position Surface form Disambiguated ID Type / Status
Subject Sendai Airport E302506 entity
Predicate hasRunway P105 FINISHED
Object Runway 09/27
Runway 09/27 is a primary paved runway at Sendai Airport in Japan, used for handling both domestic and international air traffic.
 E1016964 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 09/27 | Statement: [Sendai Airport, hasRunway, Runway 09/27]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Runway 09/27
Context triple: [Sendai Airport, hasRunway, Runway 09/27]
  • A. Runway 09/27
    Runway 09/27 is a primary paved runway at Katowice Airport in Poland, used for handling both domestic and international air traffic.
  • B. Runway 09/27
    Runway 09/27 is a primary east–west runway at Cincinnati/Northern Kentucky International Airport used for commercial air traffic operations.
  • C. Runway 09/27
    Runway 09/27 is a primary paved runway at Yao Airport in Japan, used for handling takeoffs and landings aligned roughly east–west.
  • D. Runway 09/27
    Runway 09/27 is the primary east–west runway at Trondheim Airport, Værnes, used for most commercial flight operations at the airport.
  • E. Runway 09/27
    Runway 09/27 is the principal east–west runway used for takeoffs and landings at Biarritz Pays Basque Airport in southwestern France.
  • 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 09/27
Triple: [Sendai Airport, hasRunway, Runway 09/27]
Generated description
Runway 09/27 is a primary paved runway at Sendai Airport in Japan, used for handling both domestic and international air traffic.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Runway 09/27
Target entity description: Runway 09/27 is a primary paved runway at Sendai Airport in Japan, used for handling both domestic and international air traffic.
  • A. Runway 09/27
    Runway 09/27 is a primary paved runway at Yao Airport in Japan, used for handling takeoffs and landings aligned roughly east–west.
  • B. Runway 09/27
    Runway 09/27 is a primary paved runway at Katowice Airport in Poland, used for handling both domestic and international air traffic.
  • C. Runway 09/27
    Runway 09/27 is the primary paved runway at Rzeszów–Jasionka Airport in southeastern Poland, used for both domestic and international air traffic operations.
  • D. Runway 09/27
    Runway 09/27 is a primary paved runway at Leonard M. Thompson International Airport in the Bahamas, used for handling the airport’s main aircraft operations.
  • E. Runway 09/27
    Runway 09/27 is a primary east–west runway at Cincinnati/Northern Kentucky International Airport used for commercial air traffic operations.
  • 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_69d7bded71a88190bb76e2413af9ea66 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d9617e030881908444743b8a7e0d75 completed April 10, 2026, 8:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6cbb4d0088190b71fc0573cd40ddd completed May 3, 2026, 4:14 a.m.
NEDg Description generation batch_69f6cd0d21e08190855dcbee000fc25d completed May 3, 2026, 4:20 a.m.
NED2 Entity disambiguation (via description) batch_69f6ce6b220c8190b1f49a9b2bfce692 completed May 3, 2026, 4:26 a.m.
Created at: April 9, 2026, 5:19 p.m.