Based on my prior knowledge, the feature "fibr_ter_02" represents whether the patient received fibrinolytic therapy by Celiasum 1m IU. To analyze the relationship between this feature and the task of identifying chronic heart failure, we can examine the distribution of the feature values for the two target classes.

Based on the provided task, we only need to identify if the myocardial infarction complications data of the patient shows chronic heart failure or not. Hence, we don't need to consider the specific values of the feature, but rather analyze the presence or absence of fibrinolytic therapy using Celiasum 1m IU.

Here's the dictionary representing the relationship between the feature "fibr_ter_02" and the target classes:

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

In this case, for the target class "no" (indicating no chronic heart failure), the presence of fibrinolytic therapy using Celiasum 1m IU is not expected, so the list contains only the value "no". For the target class "yes" (indicating chronic heart failure), the presence of fibrinolytic therapy using Celiasum 1m IU is expected, so the list contains only the value "yes".

This analysis assumes that there is a direct relationship between fibrinolytic therapy and chronic heart failure, where the presence of the therapy indicates the presence of chronic heart failure. It's important to note that this analysis is based on my prior knowledge and assumptions.