Triple
T11870101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harley-Davidson Road Glide |
E282383
|
entity |
| Predicate | hasFrontWheelSize |
P44248
|
FINISHED |
| Object | typically large-diameter front wheel (varies by trim) |
—
|
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: typically large-diameter front wheel (varies by trim) | Statement: [Harley-Davidson Road Glide, hasFrontWheelSize, typically large-diameter front wheel (varies by trim)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFrontWheelSize Context triple: [Harley-Davidson Road Glide, hasFrontWheelSize, typically large-diameter front wheel (varies by trim)]
-
A.
formerWheelDiameter
Indicates that one entity was previously the wheel diameter of another entity, but is no longer its current wheel diameter.
-
B.
wheelDiameter
chosen
Indicates the size of a wheel measured across its diameter.
-
C.
wheelHeightApprox
Indicates that the height of a wheel is approximately equal to a specified value or to the height of another wheel.
-
D.
tireSize
Indicates the specific size of a tire associated with an object or vehicle.
-
E.
driverDiameter
Indicates the size of the circular cross-section of a driver component, typically measured as the distance across its widest point.
- 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_69d6ab2945d081908a5851c916cbcfb5 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a73c04e4819084c0b2ff8e5d2f04 |
completed | April 10, 2026, 7:31 a.m. |
| PD | Predicate disambiguation | batch_69d8a2589f0c8190ad82ff11acabae93 |
completed | April 10, 2026, 7:10 a.m. |
Created at: April 8, 2026, 9:43 p.m.