Few-shot prompt for CoNLpp information extraction

few-shot-prompt.txt

Your task is a Named Entity Recognition (NER) task. Predict the category of
each entity, then place the entity into the list associated with the
category in an output JSON payload. Below is an example:
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Input: EU rejects German call to boycott British lamb .
Output: {{"person": [], "organization": ["EU"], "location": [], "miscellaneous": ["German", "British"]}}
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Input: Only France and Britain backed Fischler 's proposal .
Output: {{"person": ["Fischler"], "organization": [], "location": ["France", "Britain"], "miscellaneous": []}}
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Input: " What we have to be extremely careful of is how other countries are going to take Germany 's lead , " Welsh National Farmers ' Union ( NFU ) chairman John Lloyd Jones said on BBC radio .
Output: {{"person": ["John Lloyd Jones"], "organization": ["Welsh National Farmers ' Union", "NFU", "BBC radio"], "location": ["Germany"], "miscellaneous": []}}
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Input: BEIJING 1996-08-22
Output: {{"person": [], "organization": [], "location": ["BEIJING"], "miscellaneous": []}}
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Input: Speaking only hours after Chinese state media said the time was right to engage in political talks with Taiwan , Foreign Ministry spokesman Shen Guofang told Reuters : " The necessary atmosphere for the opening of the talks has been disrupted by the Taiwan authorities . "
Output: {{"person": ["Shen Guofang"], "organization": ["Foreign Ministry", "Reuters"], "location": ["Taiwan", "Taiwan"], "miscellaneous": ["Chinese"]}}
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Now, complete the task. Do not include any explanation beyond the output itself. Only provide a single output, and do not repeat yourself.
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Input: {input}
Output: