Based on prior knowledge, we can analyze the relationship between the feature "n_p_ecg_p_09" and the presence of chronic heart failure in the myocardial infarction complications data.

Assuming that "LBBB" stands for "Left Bundle Branch Block," an incomplete LBBB on an ECG at the time of admission to the hospital can be an indicator of underlying heart conditions. However, it does not directly indicate the presence of chronic heart failure.

To create the dictionary, we need to examine the possible values of the feature "n_p_ecg_p_09" for both classes, "yes" and "no."

* For the target class "no" (indicating no chronic heart failure), the possible values of the feature can be:
    - 'no': If the patient does not have an incomplete LBBB on an ECG at the time of admission to the hospital, it suggests no sign of chronic heart failure.
    - Other values: Since the class "no" indicates the absence of chronic heart failure, any other value can fall into this category as it is hard to predict based on the given feature.

* For the target class "yes" (indicating chronic heart failure), the possible values of the feature can be:
    - 'yes': If the patient has an incomplete LBBB on an ECG at the time of admission to the hospital, it suggests a possible indicator of chronic heart failure.
    - Other values: Since the class "yes" indicates the presence of chronic heart failure, any other value can fall into this category as it is hard to predict based on the given feature.

Considering the description above, the corresponding dictionary is as follows:

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

It is worth noting that if there are other possible values for the feature "n_p_ecg_p_09" that are hard to predict, they are not included in the dictionary. However, it should be ensured that the list of each target class is not empty.