Based on prior knowledge, chest pain can be a symptom of heart disease. Therefore, it is reasonable to assume that the type of chest pain, as represented by the feature ChestPainType, could be related to the presence of heart disease.

To analyze the relationship between ChestPainType and the target variable (heart disease as shown in coronary angiography), we can examine the distribution of ChestPainType for both the "yes" and "no" cases.

We can create a dictionary with the values of ChestPainType that are associated with each class. However, without specific data or insights into the relationship, it is difficult to determine in advance which ChestPainType values are "hard to predict." Therefore, we will include all possible values in the result dictionary to provide a comprehensive analysis.

Here is the generated dictionary:

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

Please note that the values ["ATA", "NAP", "ASY", "TA"] indicate all possible categories of ChestPainType, which are applicable for both the "no" and "yes" cases.