Based on my prior knowledge, a first-degree AV block on an ECG at the time of admission to the hospital can be an indicator of myocardial infarction complications leading to chronic heart failure. However, it is important to note that this relationship is not deterministic, and there are cases where chronic heart failure may not be present despite the presence of a first-degree AV block. 

To analyze the relationship between the feature "n_p_ecg_p_03" (First-degree AV block on ECG at the time of admission to hospital) and the target task of determining if the patient has chronic heart failure, we can examine the frequency of different values of "n_p_ecg_p_03" for each target class.

Let's assume we have a dataset where we can observe the target variable (presence of chronic heart failure) along with the feature "n_p_ecg_p_03". We can analyze the data and create a dictionary that shows the possible values of "n_p_ecg_p_03" for each target class.

Here is the dictionary based on the analysis:

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

The dictionary shows that for the target class "no" (indicating no chronic heart failure), the feature "n_p_ecg_p_03" only takes the value "no". However, for the target class "yes" (indicating the presence of chronic heart failure), the feature "n_p_ecg_p_03" can take both "no" and "yes" values.

Please note that this is a hypothetical example, and the actual relationship between the feature "n_p_ecg_p_03" and the presence of chronic heart failure may vary depending on the specific dataset and context.