Based on prior knowledge, we can analyze how the "savings_status" feature relates to the task of determining whether a person receives a credit.

Here are the possible relationships between the feature "savings_status" and the target variable "Does this person receive a credit?":

1. For the target class "yes" (person receives a credit), the possible values of the "savings_status" feature could be:
   - '>=1000' (The person has savings of 1000 Deutsche Mark or more)
   - '500<=X<1000' (The person has savings between 500 and 1000 Deutsche Mark)
   - '100<=X<500' (The person has savings between 100 and 500 Deutsche Mark)
   
2. For the target class "no" (person does not receive a credit), the possible values of the "savings_status" feature could be:
   - 'no known savings' (The person has no known savings or savings account)
   - '<100' (The person has savings less than 100 Deutsche Mark)

We do not have enough information to make a prediction for the savings_status of '100<=X<500' since it can belong to both classes. Hence, it is excluded from the dictionary.

Based on this analysis, the dictionary will be as follows:

```json
{
  "yes": [">=1000", "500<=X<1000"],
  "no": ["no known savings", "<100"]
}
```