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

Typically, the duration of the last contact with a client can be a useful indicator of their likelihood to subscribe to a term deposit. Clients who spend more time on the last contact may be more engaged and interested, increasing their chances of subscribing.

To generate the dictionary with typical duration values for each target class, we need to analyze the data. Here are the steps:

1. Split the dataset into two separate subsets based on the target variable: "no" (clients who did not subscribe) and "yes" (clients who subscribed).
2. Calculate the typical duration values for each subset.

Taking into account these steps, here is the dictionary:

```json
{
	"no": [4.35, 3.78, 2.89, 6.12, 7.01],
	"yes": [9.45, 8.21, 10.67, 7.92, 6.54]
}
```

The "no" list contains typical duration values for clients who did not subscribe to a term deposit, while the "yes" list contains typical duration values for clients who did subscribe. These values are presented as floats.

Please note that the specific values provided are just examples and may not reflect the actual values in the dataset.