Based on the given feature description, we have a categorical variable called "n_p_ecg_p_06" that represents the presence or absence of a third-degree AV block on ECG at the time of admission to the hospital.

To determine if this feature is related to the target variable (chronic heart failure), we need more information about the data and the relationship between the feature and the target. Without specific information on the dataset and its features, it is challenging to establish a direct relationship between "n_p_ecg_p_06" and chronic heart failure.

If we assume that the feature is relevant, we can analyze the possible values "no" and "yes" of "n_p_ecg_p_06" with respect to chronic heart failure. We would expect the feature values that indicate the presence of a third-degree AV block (i.e., "yes") to be associated with a higher likelihood of chronic heart failure.

However, without any knowledge or data on the relationship between the feature and target, we cannot conclude if the values of "n_p_ecg_p_06" can accurately predict chronic heart failure.

Given this uncertainty, we can still create a dictionary to document the possible values of "n_p_ecg_p_06" for each target class:

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

Please note that without understanding the underlying data and its characteristics, it is difficult to provide a more informative analysis.