Based on prior knowledge, the feature "own_telephone" is a categorical variable with two possible categories: "yes" and "none". 

To analyze the relationship between the feature and the task of determining whether the person receives credit or not, we can look at the following possibilities:

1. own_telephone = "yes" and receives credit: This suggests that having a telephone might be a positive factor in receiving credit. The person's ability to be contacted through a telephone could be seen as a sign of stability or responsibility.

2. own_telephone = "yes" and does not receive credit: Having a telephone does not guarantee that a person will receive credit. Other factors, such as their credit history or income, might have a more significant impact.

3. own_telephone = "none" and receives credit: This suggests that not having a telephone does not necessarily prevent someone from receiving credit. Lenders might consider other factors, like employment status or financial stability, to determine creditworthiness.

4. own_telephone = "none" and does not receive credit: The absence of a telephone may indicate a lack of stability or difficulty in communication, which could impact credit decisions.

Based on this analysis, we can generate the requested dictionary:

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

Since the feature "own_telephone" only has two possible values and we have accounted for both cases in the analysis, the lists for each target class are not empty.