Based on prior knowledge, the ST_Slope feature is commonly used to determine the presence of heart disease in patients. Therefore, analyzing the relationship between the ST_Slope feature and the target variable, which represents whether the coronary angiography shows a heart disease, can provide valuable insights.

To create a dictionary with the requested information, I will analyze the distribution of ST_Slope values for each class of the target variable.

Here is the analysis:

For target class "no" (coronary angiography does not show heart disease):
- Possible values of ST_Slope: ['Up', 'Flat', 'Down']

For target class "yes" (coronary angiography shows heart disease):
- Possible values of ST_Slope: ['Up', 'Flat', 'Down']

Since the feature and target variable are analyzed together, it appears that there are no specific ST_Slope values that can be predicted with certainty as indicative of the presence or absence of heart disease. Therefore, all possible ST_Slope values are included in the dictionary.

Based on this analysis, the dictionary will be as follows:

```json
{
	"no": ["Up", "Flat", "Down"],
	"yes": ["Up", "Flat", "Down"]
}
```