Triple
T21680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Washington Dulles International Airport |
E430
|
entity |
| Predicate | primaryRunwayLength |
P266
|
FINISHED |
| Object | 11500 ft |
—
|
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: 11500 ft | Statement: [Washington Dulles International Airport, primaryRunwayLength, 11500 ft]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryRunwayLength Context triple: [Washington Dulles International Airport, primaryRunwayLength, 11500 ft]
-
A.
runwaySurface
Indicates the type or condition of the surface material that a runway is made of or covered with.
-
B.
largestAirport
Indicates that one airport is the largest (typically by area, traffic, or capacity) among a specified set or within a given region.
-
C.
hasMajorAirport
Indicates that a location possesses at least one significant airport that serves as a primary hub for air travel in that area.
-
D.
length
chosen
Indicates a measurement relationship where a value specifies how long something is from one end to the other.
-
E.
primaryStation
Indicates that one station is designated as the main or principal station associated with another entity or within a given context.
- 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_69a243b4ac2c8190b93c303df797b7b2 |
completed | Feb. 28, 2026, 1:24 a.m. |
| NER | Named-entity recognition | batch_69a246e94ca881908f7a7d2c0b293033 |
completed | Feb. 28, 2026, 1:37 a.m. |
| PD | Predicate disambiguation | batch_69a24654724481909ba14b7f68d2a472 |
completed | Feb. 28, 2026, 1:35 a.m. |
Created at: Feb. 28, 2026, 1:34 a.m.