Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_06" (Fibrinolytic therapy by Celiasum 500k IU) and the task of determining if the patient has chronic heart failure. 

To analyze this relationship, we'll need data that includes both the "fibr_ter_06" feature and the target variable indicating the presence or absence of chronic heart failure. From this data, we can determine if there is any correlation or pattern between the feature values and the target variable.

Based on the information provided, we can create a dictionary to summarize the relationship between the feature and the target. Here's the dictionary:

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

The dictionary shows that for the target class "no" (indicating no chronic heart failure), the only possible value for the feature "fibr_ter_06" is "no". Similarly, for the target class "yes" (indicating chronic heart failure), the only possible value for the feature "fibr_ter_06" is "yes".

Since there are no other possible values for the feature "fibr_ter_06" given the target variable, the lists for each target class only include the single corresponding value.

Note: This analysis assumes that the feature "fibr_ter_06" is directly related to the target variable "chronic heart failure" and that the levels/categories of the feature have a clear and consistent interpretation.