Based on prior knowledge, we can analyze the relationship between the feature "n_r_ecg_p_03" and the target variable "chronic heart failure" to determine if the feature provides any information.

Since the feature represents ventricular contractions on ECG at the time of admission to the hospital, it is possible that the presence or absence of ventricular contractions may be indicative of chronic heart failure. 

To conduct the analysis, we need access to data that includes both the feature "n_r_ecg_p_03" and the target variable "chronic heart failure". Once we have this data, we can examine the value counts of the feature for each class of the target variable.

Based on the value counts, we can create a dictionary that lists the possible values of "n_r_ecg_p_03" for each class of the target variable.

Let's assume we have collected the data and found the following value counts:

- For the class "no" (patients without chronic heart failure), the possible values of "n_r_ecg_p_03" are ['no', 'yes'].
- For the class "yes" (patients with chronic heart failure), the possible values of "n_r_ecg_p_03" are ['no', 'yes'].

Based on this analysis, we can create the required dictionary:

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