Based on prior knowledge, we can analyze the relationship between the feature "n_p_ecg_p_09" (Incomplete LBBB on ECG at the time of admission to the hospital) and the occurrence of chronic heart failure as the target variable.

In order to determine if there is a relationship between the feature and the target, we can look at the distribution of the feature values for each class of the target variable. Specifically, we need to analyze the values of "n_p_ecg_p_09" for the two target classes: "no" (indicating no chronic heart failure) and "yes" (indicating the presence of chronic heart failure).

Here is the analysis:

For the target class "no" (no chronic heart failure):
- The feature values for this class are: ['no']

For the target class "yes" (presence of chronic heart failure):
- The feature values for this class are: ['no', 'yes']

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

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

This dictionary indicates that for the target class "no" (no chronic heart failure), the only possible value of the feature "n_p_ecg_p_09" is "no". For the target class "yes" (presence of chronic heart failure), both "no" and "yes" are possible values of the feature.

Please note that the analysis assumes the given feature description and task are accurate, and the prior knowledge about the relationship between the feature and target is valid.