Triple
T309813
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Centennial Wheel |
E6379
|
entity |
| Predicate | wheelType |
P11362
|
FINISHED |
| Object | observation Ferris wheel |
—
|
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: observation Ferris wheel | Statement: [Centennial Wheel, wheelType, observation Ferris wheel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wheelType Context triple: [Centennial Wheel, wheelType, observation Ferris wheel]
-
A.
wheelbase
Indicates the distance between the centers of the front and rear wheels of a vehicle.
-
B.
driveType
Indicates the type or configuration of the drive mechanism used to power or propel an entity.
-
C.
wheelArrangementSystem
Indicates the specific configuration or system by which the wheels of a vehicle or rolling stock are arranged and organized.
-
D.
landingGearType
Indicates the specific kind or configuration of landing gear that an object (typically an aircraft or vehicle) uses.
-
E.
vehicleType
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
- F. None of above. chosen
Provenance (4 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_69a2e79230508190b912ecb555aae17e |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2ea33ba688190b30d285cd7aa0d82 |
completed | Feb. 28, 2026, 1:14 p.m. |
| PD | Predicate disambiguation | batch_69a2e93f38308190b4b480c951f1a1c3 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2ea2af1388190b93235602ace679e |
completed | Feb. 28, 2026, 1:14 p.m. |
Created at: Feb. 28, 2026, 1:06 p.m.