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: We originate and acquire transitional loans secured by first lien mortgages on commercial real estate, and in some instances pledges of equity in the property holder. These loans provide interim financing to borrowers seeking short-term capital for the acquisition, lease up or repositioning of commercial real estate and generally have an initial maturity of three years or less. A borrower under a transitional loan has usually identified an asset that the borrower views as undervalued, having been under-managed and/or is located in a recovering market. If the borrower's assessment of the asset as undervalued is inaccurate, or if the market in which the asset is located fails to recover according to the borrower’s projections, or if the borrower fails to improve the operating performance of the asset or the value of the asset, the borrower may not receive a sufficient return on the asset to satisfy the transitional loan, and we will bear the risk that we may not recover some or all of our investment. During periods in which there are decreases in demand for certain properties as a result of macroeconomic factors, reductions in the financial resources of tenants and defaults by borrowers or tenants, borrowers face additional challenges in transitioning properties. Market downturns or other adverse macroeconomic factors may affect transitional loans in our portfolio more adversely than loans secured by more stabilized assets. A portion of our loans are secured by office space and similar commercial real estate. This sector has recently been affected by certain macroeconomic factors, such as an increased prevalence of remote work.
Assistant: