Triple
T33386921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Siemens SD-460 |
E854938
|
entity |
| Predicate | vehicleLength_category |
P168158
|
FINISHED |
| Object | approximately 27–30 m |
—
|
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: approximately 27–30 m | Statement: [Siemens SD-460, vehicleLength_category, approximately 27–30 m]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleLength_category Context triple: [Siemens SD-460, vehicleLength_category, approximately 27–30 m]
-
A.
vehicleSizeRange
chosen
Indicates the range of sizes (e.g., dimensions, capacity, or weight class) within which a vehicle falls.
-
B.
hasCarLength
Indicates that an entity is associated with a specific measurement representing the length of a car.
-
C.
carWidth
Indicates the measurement of how wide a car is across its lateral (side-to-side) dimension.
-
D.
laneLength
Indicates the length or distance of a lane in a given context.
-
E.
vehicleStandard
Indicates that something complies with, or is defined according to, a specified vehicle-related standard or regulatory specification.
- 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_69f3496d54048190a1cb91fdd7caa6ea |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f38159d08190980ad639e08f00f4 |
completed | May 3, 2026, 7:04 a.m. |
| PD | Predicate disambiguation | batch_69f6e3d7bee48190b94e0beb48a1d7fa |
completed | May 3, 2026, 5:57 a.m. |
Created at: May 1, 2026, 1:35 a.m.