Based on prior knowledge, we need to analyze the relationship between the feature "fibr_ter_01" (Fibrinolytic therapy by Celiasum 750k IU) and the target variable indicating the presence of chronic heart failure in the patient.

To do this, we can examine the distribution of "fibr_ter_01" values for each class of the target variable (yes and no). By comparing the frequency of each value of "fibr_ter_01" for the two classes, we can determine if there is any association between the feature and the target.

Here is an analysis of the relationship between "fibr_ter_01" and chronic heart failure:

- Possible values of "fibr_ter_01":

  - "no": Indicates that the patient did not receive Fibrinolytic therapy by Celiasum 750k IU.
  - "yes": Indicates that the patient received Fibrinolytic therapy by Celiasum 750k IU.

To generate the dictionary, we need to examine the distribution of "fibr_ter_01" values for each class of the target variable:

- For the target class "no":

  - Possible values of "fibr_ter_01": ["no"]
  
- For the target class "yes":

  - Possible values of "fibr_ter_01": ["no", "yes"]
  
Based on this analysis, we can create the following dictionary:

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

Please note that there could be other possible values of "fibr_ter_01" that are hard to predict or are not present in the dataset. In such cases, they are not included in the dictionary.