Based on prior knowledge, we can analyze the relationship between the feature SIM_GIPERT (Symptomatic hypertension) and the presence of chronic heart failure in the myocardial infarction complications data of a patient.

The feature SIM_GIPERT is a categorical variable with two possible categories: 'no' and 'yes'.

To determine how this feature relates to the task of identifying chronic heart failure, we would need to analyze the distribution of SIM_GIPERT values for both the positive and negative instances of chronic heart failure.

Let's assume we have a dataset with the following observations:

| SIM_GIPERT | Chronic Heart Failure |
|------------|----------------------|
| no         | no                   |
| yes        | yes                  |
| no         | yes                  |
| yes        | no                   |
| no         | no                   |
| yes        | no                   |
| yes        | yes                  |
| no         | yes                  |
| yes        | no                   |

Based on this data, we can generate the dictionary:

```json
{
	"no": ["no", "no"],
	"yes": ["yes", "yes", "no", "yes", "no"]
}
```

In this case, the feature SIM_GIPERT value "no" is observed for both the negative instances of chronic heart failure. The value "yes" is observed for both the positive and negative instances of chronic heart failure.

Please note that this is just an example analysis based on the provided dataset. The actual analysis may vary depending on the available data and the specific relationship between the feature and the target variable.