Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_01" and the target class "chronic heart failure".

Since fibr_ter_01 is a categorical variable indicating whether fibrinolytic therapy was given or not, we can analyze the distribution of the feature for the two classes of the target variable:

1. Target Class: No (chronic heart failure not present):
   - Possible values of feature "fibr_ter_01": ['no']
   
2. Target Class: Yes (chronic heart failure present):
   - Possible values of feature "fibr_ter_01": ['no', 'yes']

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

```json
{
	"no": ["no"],
	"yes": ["no", "yes"]
}
```

Please note that since the target class "yes" includes all possible values of the feature, we include both "no" and "yes" in the list of values.