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

Based on the given feature and task, the feature "own_telephone" can be analyzed as follows:

Since the task is to determine whether the person receives a credit or not, it is important to see how the feature "own_telephone" is related to this task. 

Looking at the feature, "own_telephone" is a categorical variable with two possible categories: "yes" and "none". This feature represents whether the person owns a telephone or not. 

Analyzing the relationship between this feature and the target variable, we can make the following observations:

- When the person owns a telephone ("own_telephone" = "yes"), there is a possibility that they receive a credit ("yes" for the target variable).
- On the other hand, when the person does not own a telephone ("own_telephone" = "none"), there is a possibility that they do not receive a credit ("no" for the target variable).

Based on this analysis, the dictionary is created with the possible values of the feature "own_telephone" for each target class:

- For the target class "yes", the possible value of the feature "own_telephone" is "yes".
- For the target class "no", the possible value of the feature "own_telephone" is "none".