Based on my prior knowledge, the feature "own_telephone" may have some relationship with the task of determining whether a person receives credit or not.

To analyze this relationship, we can consider the following points:
1. If the person owns a telephone ("own_telephone" = 'yes'), it might indicate a stable financial status and regular communication, which could be positive factors in receiving credit. Therefore, it is likely that a higher percentage of people who own a telephone receive credit.
2. If the person doesn't own a telephone ("own_telephone" = 'none'), it could indicate a lower financial stability or limited communication options. This might be a negative factor in receiving credit. Therefore, it is possible that a lower percentage of people who don't own a telephone receive credit.

Based on these observations, we can proceed to create the dictionary:

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

Since "own_telephone" is a categorical variable with only two possible values, we can include both categories in the dictionary. According to our analysis, if the value is "yes", it is likely that the person receives credit, and if the value is "none", it is possible that the person doesn't receive credit.