Based on prior knowledge, the relationship between the feature "ChestPainType" and the task "Does the coronary angiography of this patient show a heart disease?" can be analyzed as follows:

Chest pain can be an important symptom of heart disease, and different types of chest pain can be associated with different underlying conditions. Therefore, it is reasonable to assume that the category of "ChestPainType" might have some relationship with the presence or absence of heart disease.

To create the dictionary, we need to analyze the possible values of the "ChestPainType" feature for each target class (yes for heart disease and no for no heart disease). Here is the generated dictionary:

```json
{
	"no": ["ATA", "NAP", "TA"],  
	"yes": ["ASY"]  
}
```

In this case, the possible values of "ChestPainType" for the target class "no" are "ATA", "NAP", and "TA", indicating that patients with these chest pain types do not have heart disease. The value "ASY" represents the possible value of "ChestPainType" for the target class "yes", indicating that patients with this chest pain type may have heart disease.

Note that the values that are hard to predict, such as those not explicitly listed, are not included in the dictionary. However, make sure that each target class has a list that is not empty.