Based on prior knowledge, the feature fibr_ter_06 represents whether the patient has received Fibrinolytic therapy by Celiasum 500k IU, with categories 'no' and 'yes'. 

To analyze the relationship between this feature and the presence of chronic heart failure as the target variable, we need access to data that includes both the feature and target variables. Without access to the data, we cannot directly analyze the relationship. However, we can make certain assumptions and possibilities based on general knowledge.

Assuming that Fibrinolytic therapy by Celiasum 500k IU is not directly related to chronic heart failure, we can expect that there might not be a significant relationship between the feature fibr_ter_06 and chronic heart failure. In other words, the distribution of the feature values 'no' and 'yes' for chronic heart failure might not be drastically different.

Based on this assumption, we can create a dictionary representing this relationship:

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

In this case, the values "no" and "yes" are included in both target classes as possibilities of the feature fibr_ter_06, as we cannot predict which value is more likely to be associated with chronic heart failure without the actual data analysis.