Based on prior knowledge, the feature "own_telephone" represents whether the person has a telephone or not. 

To analyze the relationship between the feature and the task, we can examine the distribution of "own_telephone" values for the target classes "yes" (person receives credit) and "no" (person does not receive credit).

Possible values of "own_telephone" for target class "yes" (person receives credit) could be ['yes', 'none']. 

Possible values of "own_telephone" for target class "no" (person does not receive credit) could be ['yes', 'none'].

Based on this analysis, we can create the dictionary:

```json
{
	"yes": ["yes", "none"],
	"no": ["yes", "none"]
}
```