Based on my prior knowledge, a third-degree AV block is a complete block that occurs between the atria and ventricles, resulting in a loss of coordination between the two chambers. It is a serious cardiac conduction disorder that can lead to various complications, including chronic heart failure.

Considering the feature "np_04" which represents the presence of a third-degree AV block in the patient's medical history, we can analyze its relationship with the target variable, the presence of chronic heart failure.

To analyze the relationship, we would need a dataset with information on patients' medical history, including whether they have chronic heart failure and whether they have a third-degree AV block. Without access to such data, we cannot perform a direct analysis. 

However, based on the prior knowledge, we can assume the following:

1. If the feature "np_04" is "yes" (indicating the presence of a third-degree AV block), there is an increased likelihood of chronic heart failure.

2. If the feature "np_04" is "no" (indicating the absence of a third-degree AV block), there is a decreased likelihood of chronic heart failure.

Considering these assumptions, the possible values for the feature "np_04" for the target class "no" (indicating the absence of chronic heart failure) would be "no" (indicating the absence of third-degree AV block).

Similarly, the possible values for the feature "np_04" for the target class "yes" (indicating the presence of chronic heart failure) would be "yes" (indicating the presence of third-degree AV block).

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

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

Note that the dictionary only includes the possible values for each target class as mentioned above.