Triple
T1093948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Region of Waterloo International Airport |
E24228
|
entity |
| Predicate | runway14/32Length |
P6291
|
FINISHED |
| Object | 1531 metres |
—
|
LITERAL FINISHED |
How this triple was built (2 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: 1531 metres | Statement: [Region of Waterloo International Airport, runway14/32Length, 1531 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: runway14/32Length Context triple: [Region of Waterloo International Airport, runway14/32Length, 1531 metres]
-
A.
runwayLength
chosen
Indicates the length of a runway associated with an airport or airfield.
-
B.
runway
Indicates a relationship where a runway serves as the takeoff and landing surface used by aircraft at an airport or airfield.
-
C.
runwaySurface
Indicates the type or condition of the surface material that a runway is made of or covered with.
-
D.
hasRunwayLengthCategory
Indicates that an airport or airfield is associated with a specific categorical range of runway lengths (e.g., short, medium, long).
-
E.
runwayEnd
Indicates that one entity represents the end point or terminus of a runway associated with the other entity.
- F. None of above.
Provenance (3 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b99d1e8c81909cf1178d68d38885 |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b743175481908f3967e589717c55 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.