Based on prior knowledge, the relationship between the feature "ChestPainType" and the task of detecting heart disease could be as follows:

In general, certain types of chest pain, such as "NAP" (Non-Anginal Pain) or "ASY" (Asymptomatic), are less likely to be associated with heart disease. On the other hand, types like "ATA" (Atypical Angina) and "TA" (Typical Angina) are more commonly associated with heart disease.

To analyze this relationship further, we would ideally need access to a dataset that includes both the ChestPainType feature and the corresponding information of whether the coronary angiography showed heart disease or not. Based on this data, we could determine the specific values of ChestPainType that are associated with heart disease.

Without access to the dataset, we can make an assumption based on the general understanding mentioned earlier. Using this assumption, we can create the following dictionary:

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

This dictionary assumes that "NAP" and "ASY" are the chest pain types associated with a "no" (no heart disease) outcome, while "ATA" and "TA" are the chest pain types associated with a "yes" (heart disease) outcome.

Please note that this assumption is based on general knowledge, and to provide a more accurate analysis, it is necessary to have access to the specific data relating ChestPainType and the corresponding outcomes.