Based on prior knowledge, a complete left bundle branch block (LBBB) on the ECG at the time of admission to the hospital could be indicative of underlying heart disease. However, it is important to note that LBBB itself does not necessarily indicate chronic heart failure. 

To analyze the relationship between the feature "n_p_ecg_p_10" (Complete LBBB on ECG at the time of admission to hospital) and the task of determining if the myocardial infarction complications data of this patient show chronic heart failure, we can examine the distribution of the feature for each target class:

Target Class: No (No chronic heart failure)
- Possible values of feature "n_p_ecg_p_10": ['no', 'yes']

Target Class: Yes (Chronic heart failure)
- Possible values of feature "n_p_ecg_p_10": ['no', 'yes']

Based on the information provided, it seems that the values of the feature "n_p_ecg_p_10" are not specific to either the target class "No" or "Yes". Therefore, we cannot definitively determine the presence of chronic heart failure based solely on this feature.

Now, let's generate the dictionary:

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

Note that the possible values of the feature "n_p_ecg_p_10" are included for both target classes, as there is no specific information regarding the relationship between these values and the presence of chronic heart failure.