City Parser
Task: Extract and normalize city name from text. Hard requirements: - Output exactly one JSON object. No code fences, no prose, no trailing commas. - Use only the keys in the schema. Do not add comments or extra fields. - Round `confidence` to two decimals. Rules: - Extract city from phrases: "Weather in Moscow", "Погода в Москве", "Things to do in Paris" - Handle prepositions: "in", "в", "to", "для", "from", "из" - Handle pronouns with context: "there"→use context city, "here"→use context city - Normalize common abbreviations: NYC→New York, SF→San Francisco, LA→Los Angeles - Handle multilingual: Москва→Moscow, Питер→Saint Petersburg - Return confidence 0.9+ for clear cities, 0.5-0.8 for ambiguous, <0.5 for unclear - If NO city is mentioned in the text, return confidence 0.0 Confidence Calibration Guidelines: - 0.90-1.00: Clear city name with strong signal - 0.70-0.89: Clear city but with some ambiguity or context dependency - 0.50-0.69: Ambiguous city reference that could be multiple locations - 0.20-0.49: Weak city signal or potential false positive - 0.00-0.19: No clear city reference Pronoun Handling Guidelines: - When "there" or "here" is used, confidence should reflect the certainty of the context match - If context has a city, use 0.70-0.80 for pronoun resolution - If context is missing or unclear, use 0.20-0.40 for pronouns Input: "{text}" Context: {context} Output JSON only: {"city": "clean_city_name", "normalized": "normalized_name", "confidence": 0.00-1.00} Few‑shot examples: - Input: "Weather in NYC" | Context: {} → {"city":"New York","normalized":"New York","confidence":0.95} - Input: "Что делать в Питере?" | Context: {} → {"city":"Saint Petersburg","normalized":"Saint Petersburg","confidence":0.90} - Input: "Go there in summer" | Context: {"city":"Tokyo"} → {"city":"Tokyo","normalized":"Tokyo","confidence":0.70} - Input: "What to do there?" | Context: {} → {"city":"","normalized":"","confidence":0.30} - Input: "is it hot?" | Context: {"city":"Paris"} → {"city":"Paris","normalized":"Paris","confidence":0.60} - Input: "Погода в Москве" | Context: {} → {"city":"Moscow","normalized":"Moscow","confidence":0.95} - Input: "I love it here" | Context: {"city":"London"} → {"city":"London","normalized":"London","confidence":0.75} - Input: "What's the weather like there?" | Context: {"city":"Berlin"} → {"city":"Berlin","normalized":"Berlin","confidence":0.70} - Input: "Can you tell me about here?" | Context: {} → {"city":"","normalized":"","confidence":0.25} - Input: "Is it crowded there in June?" | Context: {"city":"Rome"} → {"city":"Rome","normalized":"Rome","confidence":0.80} - Input: "Tell me more about that place" | Context: {"city":"Madrid"} → {"city":"Madrid","normalized":"Madrid","confidence":0.65} - Input: "What should I do in that city?" | Context: {"city":"Barcelona"} → {"city":"Barcelona","normalized":"Barcelona","confidence":0.85}
fill the variables
This prompt has 19 variables. Pro fills them into a ready-to-paste prompt for you — no manual find-and-replace.
{text}{context}{"city":"New York","normalized":"New York","confidence":0.95}{"city":"Tokyo"}{"city":"Tokyo","normalized":"Tokyo","confidence":0.70}{"city":"","normalized":"","confidence":0.30}{"city":"Paris"}{"city":"Paris","normalized":"Paris","confidence":0.60}{"city":"Moscow","normalized":"Moscow","confidence":0.95}{"city":"London"}{"city":"London","normalized":"London","confidence":0.75}{"city":"Berlin"}{"city":"Berlin","normalized":"Berlin","confidence":0.70}{"city":"","normalized":"","confidence":0.25}{"city":"Rome"}{"city":"Rome","normalized":"Rome","confidence":0.80}{"city":"Madrid"}{"city":"Madrid","normalized":"Madrid","confidence":0.65}{"city":"Barcelona"}
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Community prompt sourced from the open-source GitHub repo chernistry/voyant (NOASSERTION). A "City Parser" 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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