Based on prior knowledge, the presence of Complete LBBB on ECG (n_p_ecg_p_10) can be indicative of chronic heart failure in patients with myocardial infarction complications.

To analyze the relationship between the feature and the task, we can examine the distribution of n_p_ecg_p_10 values for each target class. Let's assume the target class for chronic heart failure is labeled as "yes".

Potential dictionary based on the analysis:

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

Since n_p_ecg_p_10 is a categorical variable and there are only two possible categories "no" and "yes", the values for the feature are straightforward. The dictionary shows that "no" in n_p_ecg_p_10 corresponds to the target class "no" (no chronic heart failure), and "yes" in n_p_ecg_p_10 corresponds to the target class "yes" (chronic heart failure).

Please note that if there are additional possible values for n_p_ecg_p_10, it is essential to include them in the respective target class list. However, in this case, where the feature only has "no" and "yes" as possible categories, the dictionary includes all the values.