Based on prior knowledge, we know that an incomplete left bundle branch block (LBBB) on the electrocardiogram (ECG) can be a potential indicator of myocardial infarction complications and may be associated with chronic heart failure.

To 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 task of determining whether the patient shows chronic heart failure, we need to review the available data or studies that investigate this relationship.

Without additional information or data, it is difficult to definitively determine how "n_p_ecg_p_09" relates to chronic heart failure. However, we can propose two possible scenarios:

Scenario 1:
- If the available data or studies indicate that patients with "yes" values for "n_p_ecg_p_09" are more likely to develop chronic heart failure, the dictionary would look like this:

```json
{
	"no": ["no"],
	"yes": ["yes"]
}
```
In this scenario, we assume that only patients with "yes" values for "n_p_ecg_p_09" are associated with chronic heart failure.

Scenario 2:
- If the available data or studies indicate that patients with either "no" or "yes" values for "n_p_ecg_p_09" can develop chronic heart failure, the dictionary would look like this:

```json
{
	"no": ["no"],
	"yes": ["no", "yes"]
}
```
In this scenario, we assume that both patients with "no" and "yes" values for "n_p_ecg_p_09" can be associated with chronic heart failure.

Please note that the specific values of "n_p_ecg_p_09" are not known, and different datasets or studies may have different findings. The generated dictionaries provide possible relationships between the feature and the target variable, but without actual data, it is not possible to confirm the accuracy or validity of these relationships.