Triple
T35575336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz EQC |
E1028058
|
entity |
| Predicate | torqueApprox |
P132864
|
FINISHED |
| Object | 760 Nm |
—
|
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: 760 Nm | Statement: [Mercedes-Benz EQC, torqueApprox, 760 Nm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: torqueApprox Context triple: [Mercedes-Benz EQC, torqueApprox, 760 Nm]
-
A.
torque
Indicates a rotational force applied by one entity on another around a pivot or axis.
-
B.
torqueDistribution
Indicates how torque or rotational force is apportioned among multiple components, such as wheels, axles, or motors, within a system.
-
C.
torqueRange
Indicates the range of torque values within which an action, interaction, or mechanical relationship is valid or operates.
-
D.
improvedTorqueComparedTo
Indicates that one entity provides greater torque performance than another entity used as a reference.
-
E.
approximateEstimation
chosen
Indicates an estimation relationship where one value or assessment is only roughly or closely, but not exactly, equal to another.
- 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_69f76e0386688190b931bacdc145938c |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79ec355048190af30123ceb6efa2b |
completed | May 3, 2026, 7:15 p.m. |
| PD | Predicate disambiguation | batch_69f79e4bdbcc8190be7a0d2cf8a77b64 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 3, 2026, 4:04 p.m.