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: Our results of operations and financial positions have been negatively affected by our acceptance of fraudulent bookings made using credit and debit cards or fraudulently obtained loyalty points. We are sometimes held liable for accepting fraudulent bookings on our websites or other bookings for which payment is subsequently disputed by our customers both of which lead to the reversal of payments received by us for such bookings (referred to as a “charge back”). In addition, the payment card networks have rules around acceptable charge back ratios. Accordingly, we calculate and record an allowance for the resulting credit and debit card charge backs. Our ability to detect and combat fraudulent schemes, which have become increasingly common and sophisticated, may be negatively impacted by the adoption of new payment methods, the emergence and innovation of new technology platforms, including smartphones, tablet computers and in-home assistants, and our global expansion, including into markets with a history of elevated fraudulent activity. If we are unable to effectively combat fraudulent bookings on our websites or mobile applications or if we otherwise experience increased levels of charge backs, we may be subject to fines and higher transaction fees or be unable to continue to accept card payments because payment card networks have revoked our access to their networks, and our results of operations and financial positions could be materially adversely affected.
Assistant: