Triple

T11626529
Position Surface form Disambiguated ID Type / Status
Subject Mather Airport E276284 entity
Predicate runway P1654 FINISHED
Object Runway 04R/22L
Runway 04R/22L is one of the primary paved runways at Mather Airport, used for aircraft takeoffs and landings.
 E963510 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 04R/22L | Statement: [Mather Airport, runway, Runway 04R/22L]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Runway 04R/22L
Context triple: [Mather Airport, runway, Runway 04R/22L]
  • A. Runway 04R/22L
    Runway 04R/22L is one of the main parallel runways at Nice Côte d’Azur Airport, serving commercial air traffic on the French Riviera.
  • B. Runway 04R/22L
    Runway 04R/22L is one of the primary paved runways used for aircraft takeoffs and landings at Sharm El Sheikh International Airport in Egypt.
  • C. Runway 04R/22L
    Runway 04R/22L is one of the primary paved runways used for aircraft takeoffs and landings at Venice Marco Polo Airport in Italy.
  • D. Runway 04R/22L
    Runway 04R/22L is one of the primary paved runways used for aircraft takeoffs and landings at Clinton National Airport in Little Rock, Arkansas.
  • E. Runway 04L/22R
    Runway 04L/22R is one of the main parallel runways at Nice Côte d’Azur Airport, serving commercial air traffic on the French Riviera.
  • 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 04R/22L
Triple: [Mather Airport, runway, Runway 04R/22L]
Generated description
Runway 04R/22L is one of the primary paved runways at Mather Airport, used for aircraft takeoffs and landings.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Runway 04R/22L
Target entity description: Runway 04R/22L is one of the primary paved runways at Mather Airport, used for aircraft takeoffs and landings.
  • A. Runway 04R/22L
    Runway 04R/22L is one of the main parallel runways at Nice Côte d’Azur Airport, serving commercial air traffic on the French Riviera.
  • B. Runway 04R/22L
    Runway 04R/22L is one of the primary paved runways used for aircraft takeoffs and landings at Venice Marco Polo Airport in Italy.
  • C. Runway 04R/22L
    Runway 04R/22L is one of the primary paved runways used for aircraft takeoffs and landings at Clinton National Airport in Little Rock, Arkansas.
  • D. Runway 04R/22L
    Runway 04R/22L is one of the primary paved runways used for aircraft takeoffs and landings at Sharm El Sheikh International Airport in Egypt.
  • E. Runway 04L/22R
    Runway 04L/22R is one of the main parallel runways at Nice Côte d’Azur Airport, serving commercial air traffic on the French Riviera.
  • 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_69d6aafa51148190ab84940694c00235 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a12416908190ac2dcd7f7ebb308f completed April 10, 2026, 7:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69f5f631efb4819096ad6ee0c87fa7a7 completed May 2, 2026, 1:03 p.m.
NEDg Description generation batch_69f5fe53d47c8190896a9abf8cc4bc31 completed May 2, 2026, 1:38 p.m.
NED2 Entity disambiguation (via description) batch_69f5ffc563a08190b95db768df475a3a completed May 2, 2026, 1:44 p.m.
Created at: April 8, 2026, 9:39 p.m.