Based on prior knowledge, we can analyze the relationship between the feature "pdays" and the task of determining whether a client subscribes to a term deposit.

Typically, if a client was last contacted a long time ago, it may have a negative impact on their decision to subscribe to a term deposit. On the other hand, if a client was recently contacted, they may be more likely to subscribe.

To analyze the relationship, we can examine the distribution of "pdays" values for each target class ("no" and "yes") and identify typical values for each class.

Here is the dictionary with the requested information:

```json
{
	"no": [999, 998, 997, 996, 995],
	"yes": [1, 2, 3, 4, 5]
}
```

For the target class "no", which indicates that the client did not subscribe to a term deposit, some typical "pdays" values could be [999, 998, 997, 996, 995]. These values suggest that the clients were last contacted a long time ago.

For the target class "yes", which indicates that the client subscribed to a term deposit, some typical "pdays" values could be [1, 2, 3, 4, 5]. These values suggest that the clients were recently contacted.

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