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: Demand for our online ride-hailing services is sensitive to ride fares, which takes into consideration, among other things, incentives paid to online ride-hailing drivers and our service fees. Our pricing strategies could be affected by a number of factors, including operating costs, legal and regulatory requirements or constraints, our current and future competitors’ pricing and marketing strategies, the principal requirement of pricing model of our cooperated platforms, and the perception of ride fares as a non-compensatory sharing of travel cost by online ride-hailing drivers. Some competitors offer, or may in the future offer, lower-priced services. Similarly, some competitors may use marketing strategies to attract or retain riders and qualified online ride-hailing drivers at lower costs than us. Certain competitors may also attract and retain riders and qualified online ride-hailing drivers with significant subsidies. As such, we may be forced by competition, regulation or other reasons to reduce ride fares and service fees, increase incentives we pay to online ride-hailing drivers on our platform, reduce our service fees, or to increase our marketing and other expenses. Furthermore, our users’ price sensitivity may vary by geographic locations, and as we expand, our pricing methodologies may not enable us to compete effectively in these locations. We may launch new pricing strategies and initiatives, or modify existing pricing methodologies, any of which may not ultimately be successful in attracting and retaining riders and qualified online ride-hailing drivers.
Assistant: