System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User: Investment in loans held for investment is subject to a high degree of credit risk through exposure to loss from loan defaults. Default rates are subject to a wide variety of factors, including, but not limited to, borrower financial condition, property performance, property management, supply/demand factors, construction trends, consumer behavior, regional economics, interest rates, the strength of the U.S. economy and other factors beyond our control, all of which have and may continue to be detrimentally impacted by the COVID-19 pandemic. All loans are subject to a certain probability of default. We manage credit risk through the underwriting process, acquiring investments at the appropriate discount to face value, if any, and establishing loss assumptions. Performance of the loans is carefully monitored, including those held through joint venture investments, as well as external factors that may affect their value._We are also subject to the credit risk of the tenants in our properties, including business closures, occupancy levels, meeting rent or other expense obligations, lease concessions, and ESG standards and practices among other factors, all of which have and may continue to be detrimentally impacted by the COVID-19 pandemic. We seek to undertake a rigorous credit evaluation of the tenants prior to acquiring properties. This analysis includes an extensive due diligence investigation of the tenants’ businesses, as well as an assessment of the strategic importance of the underlying real estate to the respective tenants’ core business operations. Where appropriate, we may seek to augment the tenants’ commitment to the properties by structuring various credit enhancement mechanisms into the underlying leases. These mechanisms could include security deposit requirements or guarantees from entities that are deemed credit worthy._
Assistant: