Based on prior knowledge, we can analyze the relationship between the feature "n_p_ecg_p_11" and the task of determining whether the patient has chronic heart failure.

In this case, "n_p_ecg_p_11" represents the presence or absence of Incomplete RBBB (Right Bundle Branch Block) on the patient's Electrocardiography (ECG) at the time of admission to the hospital.

To determine whether this feature is related to chronic heart failure, we can consider the following aspects:

1. Clinical Research: Previous research studies or medical literature that specifically investigate the association between Incomplete RBBB on ECG and chronic heart failure could provide valuable insights. Reviewing such studies can help to determine if there is a documented relationship between the feature and the target.

2. Medical Knowledge: Understanding the pathophysiology and mechanisms of chronic heart failure can provide insights into the relevance of Incomplete RBBB on ECG as a potential indicator. Analyzing the physiological significance and potential connections between these variables can aid in determining their relationship.

3. Previous Medical Diagnoses: Reviewing previous medical diagnoses for patients with chronic heart failure may reveal any observed correlations or patterns between the feature "n_p_ecg_p_11" and the presence of chronic heart failure.

Following this analysis, we can create a dictionary as requested:

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

Since "n_p_ecg_p_11" is a categorical variable with only two possible categories ('no' and 'yes'), there is no need to exclude any specific values. However, if there were additional hard-to-predict values, we could omit them from the dictionary to ensure that each list is not empty.