Triple
T328271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steven F. Udvar-Hazy Center |
E6567
|
entity |
| Predicate | hasRunwayView |
P9193
|
FINISHED |
| Object | views of Washington Dulles International Airport runways |
—
|
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: views of Washington Dulles International Airport runways | Statement: [Steven F. Udvar-Hazy Center, hasRunwayView, views of Washington Dulles International Airport runways]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRunwayView Context triple: [Steven F. Udvar-Hazy Center, hasRunwayView, views of Washington Dulles International Airport runways]
-
A.
hasRunwayNumber
Indicates that an airport or airfield runway is assigned a specific identifying number.
-
B.
hasRunwayOrientation
Indicates that a runway is aligned or oriented in a specific directional heading.
-
C.
runway
Indicates a relationship where a runway serves as the takeoff and landing surface used by aircraft at an airport or airfield.
-
D.
hasScenicViewOf
chosen
Indicates that one entity offers a visually appealing or picturesque view of another entity.
-
E.
runwayLength
Indicates the length of a runway associated with an airport or airfield.
- 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_69a2e7933d6c8190bb2592ad13286ef2 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea98fa2c8190a5b44f4a26543a17 |
completed | Feb. 28, 2026, 1:16 p.m. |
| PD | Predicate disambiguation | batch_69a2e94aab1c8190b8654708c87eeb91 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.