Based on my prior knowledge, the feature "fibr_ter_06" represents whether or not the patient received Fibrinolytic therapy by Celiasum 500k IU. To analyze the relationship between this feature and the presence of chronic heart failure, we need to examine the distribution of the feature values for each class of the target variable.

Here is the analysis:

For target class "no" (indicating no chronic heart failure), possible values of the feature "fibr_ter_06" are:
- "no"

For target class "yes" (indicating presence of chronic heart failure), possible values of the feature "fibr_ter_06" are:
- "no"
- "yes"

Based on this analysis, we can create the following dictionary:

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

Please note that the values of the feature "fibr_ter_06" that are hard to predict are excluded from the dictionary, but each list for both target classes is not empty.