Based on prior knowledge, the relationship between the ChestPainType feature and the target variable (heart disease) can be analyzed as follows:

Different types of chest pain may indicate different underlying causes. In the context of heart disease, some types of chest pain may be more indicative of the presence of the disease than others. However, without further information or a specific medical dataset, it is difficult to determine the exact relationship between the ChestPainType feature and the heart disease target variable.

Since the task requires creating a dictionary containing the possible values of the ChestPainType feature for each target class (yes or no), we can assume that 'yes' represents the presence of heart disease and 'no' represents the absence of heart disease.

Based on this assumption, without any additional information, we cannot accurately determine the possible values of the ChestPainType feature for each target class. However, I can create a dictionary format simply assuming all possible values for the ChestPainType feature for each target class:

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

Please note that this dictionary assumes that all categories of the ChestPainType feature are relevant for both the target classes. In reality, some categories may have a stronger association with heart disease than others. However, without specific knowledge about the data or a dataset, it is not possible to provide a more accurate analysis.