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- Input cluster JSON:
- {cluster_json}
- You MUST extract a news signal from the headline AND summary. Do not leave entities empty when the text mentions obvious names.
- Task:
- 1) infer the best top-level topic
- 2) extract concise entities from the cluster
- 3) assign sentiment from the wording/context
- 4) provide short keywords that justify the classification
- Entity rules (strict):
- - Use short strings (1-5 words).
- - Include all obvious named entities mentioned in headline or summary: people, countries, regions, organizations, ministries, presidents, leaders, wars/conflicts if named.
- - Also include finance/crypto entities when present: BTC, ETH, Bitcoin, Ethereum, ETF, SEC, ECB, Fed, euro, inflation, rates.
- - Prefer canonical entity forms over aliases when obvious (for example, use full organization or place names where helpful).
- - Do NOT return empty entities if any such names/places appear.
- Sentiment rules:
- - positive: clearly encouraging, improving, or supportive tone
- - negative: clearly alarming, worsening, severe, conflict, loss, risk, warning tone
- - neutral: factual, balanced, or mixed
- - sentimentScore must be a number from -1.0 to 1.0 and should reflect the sentiment label.
- Return STRICT JSON with EXACT keys only:
- { topic, entities, sentiment, sentimentScore, keywords }
- where topic is one of [crypto, macro, regulation, ai, other].
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