Based on the given feature "n_r_ecg_p_06" and the task of determining if the patient is showing chronic heart failure, we need to analyze the relationship between the feature and the target variable.

The feature "n_r_ecg_p_06" represents the presence of persistent form of atrial fibrillation on ECG at the time of admission to the hospital. To determine the relationship between this feature and chronic heart failure, we would need to examine the data and check if there is any correlation or pattern observed.

To generate the dictionary, we would need to analyze the data by considering the values of "n_r_ecg_p_06" for the patients who show chronic heart failure (target class "yes") and those who do not show chronic heart failure (target class "no").

After analyzing the data, let's assume the following observations:

For the target class "no" (patients without chronic heart failure), the possible values of "n_r_ecg_p_06" could be ['no', 'yes'].
For the target class "yes" (patients with chronic heart failure), the possible values of "n_r_ecg_p_06" could be ['no', 'yes'].

We include both possible values of "n_r_ecg_p_06" in both target classes.

Based on this analysis, we can generate the dictionary as follows:

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

Please note that this is a hypothetical analysis based on the assumptions made. The actual analysis would require analyzing the data to determine the relationship between the feature and the target variable.