Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_03" and the task of determining whether the patient has chronic heart failure.

To analyze this relationship, we can look at the distribution of the feature values for both the "no" and "yes" target classes. By examining these distributions, we can identify patterns or trends that might indicate a relationship between the feature and the presence of chronic heart failure.

Let's assume we have a dataset with the following entries for the feature "fibr_ter_03" and the target variable indicating the presence of chronic heart failure:
```
fibr_ter_03   |   Chronic Heart Failure
----------------------------------------
no                          |          no
no                          |          no
yes                         |          no
yes                         |          yes
no                          |          yes
yes                         |          no
```

From the dataset, we can observe that when the value of "fibr_ter_03" is "no", the target variable is always "no". On the other hand, when the value of "fibr_ter_03" is "yes", the target variable can be either "no" or "yes".

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

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

This dictionary indicates that for the target class "no", the possible values of "fibr_ter_03" are ["no"]. And for the target class "yes", the possible values of "fibr_ter_03" are ["no", "yes"].

Note that we have included all possible values of "fibr_ter_03" for both target classes, even though in this specific dataset, we only have two possible values.