Based on my prior knowledge, let's analyze the relationship between the feature "duration" and the task of whether the client subscribes to a term deposit or not.

It is reasonable to assume that the duration of the last contact with the client may play a role in determining if they subscribe to a term deposit or not. Generally, a longer duration of the last contact might indicate a deeper engagement with the client and potentially increase the chances of subscribing to a term deposit. 

To generate the requested dictionary, let's find 5 typical values of the feature "duration" for each target class:

"No" class (clients who did not subscribe to a term deposit):
- [150.5, 180.0, 220.5, 300.0, 350.0]

"Yes" class (clients who subscribed to a term deposit):
- [400.0, 420.5, 500.0, 560.5, 600.0]

Based on this analysis, here is the dictionary in Markdown format:

```json
{
	"no": [150.5, 180.0, 220.5, 300.0, 350.0],
	"yes": [400.0, 420.5, 500.0, 560.5, 600.0]
}
```

Please note that the specific values provided above are just examples and may not be accurate or representative of the actual dataset.