Triple
T9234839
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BMW X7 M60i |
E221909
|
entity |
| Predicate | wheelOptions |
P11362
|
FINISHED |
| Object | large-diameter M light-alloy wheels |
—
|
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: large-diameter M light-alloy wheels | Statement: [BMW X7 M60i, wheelOptions, large-diameter M light-alloy wheels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: wheelOptions Context triple: [BMW X7 M60i, wheelOptions, large-diameter M light-alloy wheels]
-
A.
wheelType
chosen
Indicates the specific kind or category of wheel associated with an entity.
-
B.
wheelName
Indicates that an entity has or is associated with a specific name assigned to a wheel.
-
C.
wheelArrangementSystem
Indicates the specific configuration or system by which the wheels of a vehicle or rolling stock are arranged and organized.
-
D.
strapOption
Indicates a relationship where one entity offers or specifies a particular strap configuration or choice available for another entity.
-
E.
wheelDiameter
Indicates the size of a wheel measured across its diameter.
- 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_69ca83ed628c8190bc02d641e57f097f |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccee1cca0c8190bf587f54236c9e45 |
completed | April 1, 2026, 10:06 a.m. |
| PD | Predicate disambiguation | batch_69cc7a3daeb481908b0abde3fbc1f1f0 |
completed | April 1, 2026, 1:51 a.m. |
Created at: March 30, 2026, 7:29 p.m.