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 believe that recent government budgetary and spending priorities and enhancements in technology have resulted in a decrease in government office use for employees. Furthermore, over the past several years, government tenants have reduced their space utilization per employee and consolidated government tenants into existing government owned properties. This activity has reduced the demand for government leased space. Our historical experience with respect to properties of the type we own that are majority leased to government tenants has been that government tenants frequently renew leases to avoid the costs and disruptions that may result from relocating their operations. However, efforts to manage space utilization rates may result in our tenants exercising early termination rights under our leases, vacating our properties upon expiration of our leases in order to relocate, or renewing their leases for less space than they currently occupy. Also, our government tenants’ desire to reconfigure leased office space to manage utilization per employee may require us to spend significant amounts for tenant improvements, and tenant relocations are often more prevalent in those circumstances. Increasing uncertainty with respect to government agency budgets and funding to implement relocations, consolidations and reconfigurations has resulted in delayed decisions by some of our government tenants and their reliance on short term lease renewals; however, recent activity prior to the outbreak of the COVID-19 pandemic suggested that the U.S. government had begun to shift its leasing strategy to include longer term leases and was actively exploring 10 to 20 year lease terms at renewal, in some instances. It is also possible that as a result of the COVID-19 pandemic, government tenants may seek to manage space utilization rates in order to provide greater
Assistant: