Nlp City Extraction
Task: Extract a single city name. Hard requirements: - Return exactly ONE line with ONLY the city name. No labels, no quotes, no punctuation. - If no city is found, return an empty line. Rules: - Handle abbreviations (NYC→New York City, SF→San Francisco, LA→Los Angeles) - Support multilingual city names (e.g., Москва→Moscow) - Strip contaminating phrases like "pack for", "weather in", etc. Confidence Calibration Guidelines: - High confidence (0.80-1.00): Clear city name with strong context - Medium confidence (0.50-0.79): City name with some ambiguity - Low confidence (0.20-0.49): Weak or potential city reference - Very low confidence (0.00-0.19): No clear city reference Context Handling Guidelines: - When "here/there" is used, use context city with confidence 0.70-0.80 - If context is missing or unclear, return empty string with low confidence - For multilingual inputs, preserve confidence but ensure accurate translation Examples: - "What's the weather in Paris?" → "Paris" - "Pack for NYC in winter" → "New York" - "What to do in San Francisco?" → "San Francisco" - "Москва weather" → "Moscow" - "Tell me about travel" → "" Context-Aware Examples: - "What's the weather there?" (context: {"city": "Tokyo"}) → "Tokyo" - "I love it here" (context: {"city": "London"}) → "London" - "Is it crowded there in June?" (context: {"city": "Rome"}) → "Rome" - "What should I do in that city?" (context: {"city": "Barcelona"}) → "Barcelona" - "Can you tell me about here?" (context: {}) → "" - "What's the weather like there?" (context: {}) → "" User message: {message} Output: (one line, city only or empty) Edge cases: - If message contains no location, return an empty string. - If message says "here/there" and context has city, prefer context city. - For ambiguous references like "there", use context when available. - For multilingual queries, translate city names while preserving confidence levels. - When multiple cities are mentioned, return the most relevant one based on context. - Handle cases where city names might be part of larger phrases or sentences. - For abbreviations not in the standard list, attempt to resolve based on context.
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{"city": "Tokyo"}{"city": "London"}{"city": "Rome"}{"city": "Barcelona"}{message}
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Community prompt sourced from the open-source GitHub repo chernistry/voyant (NOASSERTION). A "Nlp City Extraction" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.
tags
productivitycommunitydeveloper
source
chernistry/voyant · NOASSERTION
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