Runway 12/30
E915780
Runway 12/30 is a primary paved runway at Waterloo Regional Airport in Iowa, used for commercial, general aviation, and occasional cargo operations.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Runway 12/30 canonical | 1 |
Statements (18)
| Predicate | Object |
|---|---|
| instanceOf |
airport runway
ⓘ
runway end ⓘ runway end ⓘ |
| hasPavedSurface | true ⓘ |
| hasRunwayEnd |
Runway end 12
ⓘ
Runway end 30 ⓘ |
| isPrimaryPavedRunwayAt | Waterloo Regional Airport NERFINISHED ⓘ |
| isPrimaryRunwayOf | Waterloo Regional Airport NERFINISHED ⓘ |
| locatedIn |
Black Hawk County, Iowa
NERFINISHED
ⓘ
Iowa ⓘ United States of America ⓘ
surface form:
United States
Waterloo, Iowa NERFINISHED ⓘ |
| partOf | Waterloo Regional Airport NERFINISHED ⓘ |
| runwayDesignation | 12/30 ⓘ |
| surfaceType | asphalt ⓘ |
| usedFor |
cargo operations
ⓘ
commercial aviation ⓘ general aviation ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Runway 12/30 Description of subject: Runway 12/30 is a primary paved runway at Waterloo Regional Airport in Iowa, used for commercial, general aviation, and occasional cargo operations.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.