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: Geopolitical and macroeconomic events have impacted consumer spending and our costs of operations and may continue to do so for some time in the future. Dining out is a discretionary expenditure that is influenced by domestic and global economic conditions, including, but not limited to: geopolitical instability, including armed conflicts, supply shortages, interest rates (including recent interest rates above historical norms), unemployment, significant cost inflation, health emergencies including the COVID-19 pandemic, consumer confidence, consumer purchasing and saving habits, credit conditions, stock market performance, home values, population growth, household incomes and tax policy. Material changes to governmental policy related to domestic and international fiscal concerns, and/or changes in central bank policies with respect to monetary policy, also could affect consumer discretionary spending. Any factor affecting consumer discretionary spending may influence customer traffic in our restaurants and average check amount, thus potentially having a material impact on our financial performance. Furthermore, negative economic conditions resulting from war, terrorist activities, global economic occurrences or trends or other geopolitical events or conflicts could cause consumers to make long-term changes to their discretionary spending behavior, whether on a temporary, extended or permanent basis. Reductions in staff levels and restaurant closures could result from prolonged negative economic conditions, which could materially adversely affect our business, financial condition or results of operations.
Assistant: