Based on prior knowledge, we can analyze the relationship between the feature "n_p_ecg_p_04" and the task of determining whether the patient has chronic heart failure.

In the dataset, the feature "n_p_ecg_p_04" represents the presence or absence of Type 1 Second-degree AV block (Mobitz I/Wenckebach) on the ECG at the time of admission to the hospital. We need to analyze how this feature relates to the presence of chronic heart failure.

To do this, we can examine the values of the feature "n_p_ecg_p_04" for each target class (chronic heart failure or no chronic heart failure). By comparing the values in each class, we can determine if there is any association between the feature and the presence of chronic heart failure.

Based on the analysis, we can create a dictionary with the possible values of the feature "n_p_ecg_p_04" for each target class:

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

In this case, the target class "no" includes only one possible value for the feature "n_p_ecg_p_04," which is "no." The target class "yes," on the other hand, includes both "no" and "yes" as possible values for the feature "n_p_ecg_p_04."

This suggests that the presence of Type 1 Second-degree AV block (Mobitz I/Wenckebach) on the ECG at the time of admission to the hospital (feature "n_p_ecg_p_04") is not a strong indicator of chronic heart failure, as it can be present in both cases with and without chronic heart failure.

Note: It is important to note that this analysis is based on prior knowledge and assumptions. It is always recommended to validate the findings with appropriate statistical analysis on real data.