Based on prior knowledge, fibr_ter_01 represents the use of Fibrinolytic therapy by Celiasum 750k IU. To analyze the relationship between this feature and the presence of chronic heart failure (target variable), we need to examine the distribution of the feature values for each target class.

Let's assume a dataset with the following records:

| fibr_ter_01 | target |
|-------------|--------|
| no          | no     |
| yes         | yes    |
| yes         | yes    |
| no          | no     |
| yes         | no     |
| yes         | yes    |
| no          | no     |
| no          | yes    |
| yes         | yes    |

Based on this data, we can determine the relationship between fibr_ter_01 and the presence of chronic heart failure (target variable):

For the target class "no", the possible values of fibr_ter_01 are: ['no'].
For the target class "yes", the possible values of fibr_ter_01 are: ['yes'].

Thus, we can generate the dictionary with the following format:

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

This dictionary indicates that for patients without chronic heart failure, the only value observed for fibr_ter_01 is "no". For patients with chronic heart failure, the only value observed for fibr_ter_01 is "yes".