Based on prior knowledge, it is difficult to predict the exact relationship between the "savings_status" feature and the task of whether a person receives a credit or not. However, we can make some assumptions based on intuition.

There can be different patterns observed in the relationship between savings_status and the likelihood of receiving credit depending on the dataset used. For example, it is possible that people with higher savings (categories ">=1000" and "500<=X<1000") are more likely to receive credit compared to those with lower savings (categories "no known savings", "<100", and "100<=X<500"). On the other hand, it is also possible that having no savings or very low savings is an indication of financial instability, which could make it less likely for a person to receive credit.

To analyze the relationship more accurately, we would need access to a dataset containing information about the savings_status feature and whether the person receives credit or not. From this dataset, we could calculate the percentage of people receiving credit within each savings_status category. This analysis would provide a more robust understanding of the relationship between the feature and the target.

Without the dataset, we cannot provide an exact dictionary with specific details. However, based on the assumptions mentioned above, a possible output dictionary could be as follows:

```json
{
    "yes": ["500<=X<1000", ">=1000"],  
    "no": ["no known savings", "<100", "100<=X<500"]
}
```

Please note that this dictionary is purely speculative and would require an actual dataset to validate the relationship between the feature and the task.