prompt

prompts/extract_entities.prompt

@@ -1,41 +1,35 @@
-Extract a news signal from the headline AND summary. Never return empty entities if names appear in the text.
-
-Return STRICT JSON with EXACT keys: { topic, entities, sentiment, sentimentScore, keywords }
-
-FIELDS:
-- topic: one of [crypto, macro, regulation, ai, other]
-- entities: named people, places, orgs, conflicts, and finance/crypto terms
-  (BTC, ETH, ETF, SEC, ECB, Fed, euro, inflation, rates). Canonical forms. 1–5 words each. 
-  Good: "USS Gerald Ford", "Microsoft", "Paris"; 
-  Bad: "american aircraft carrier", "software company", "french capital"
-- entites include all proper names, company names, location names
-
-- sentiment: "positive" | "negative" | "neutral"
-- sentimentScore: float –1.0 to 1.0, consistent with sentiment label
-- keywords: 2–4 thematic tags, 1–2 words each. Noun phrases only (e.g. "drone strikes",
-  "nuclear plant"). Not entity names. Not verb phrases. Not headline fragments.
-
-TASKS:
-1. Infer the best topic.
-2. Extract all named entities from headline and summary. No general categories.
-3. Assign sentiment from tone and wording.
-4. Choose keywords that capture themes, not entities.
-
-EXAMPLE:
-Input:  { "headline": "ECB raises rates again as eurozone inflation stays elevated",
-          "summary": "The European Central Bank increased its benchmark rate by 25bps,
-                      citing persistent inflation across the eurozone." }
-
-Output: { "topic": "macro",
-          "entities": ["ECB", "European Central Bank", "eurozone", "inflation", "rates"],
-          "sentiment": "negative",
-          "sentimentScore": -0.4,
-          "keywords": ["rate hike", "monetary policy"] }
-
-
-TIPS:
-- Prefer canonical entity forms over aliases when obvious (for example, use full organization or place names where helpful).
-
-INPUT:
+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: 
+  named people, , named locations, 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.
+
+Keyword rules (strict):
+- Each keyword MUST be 1-2 words. Never 3+.
+- Keywords are thematic search tags, NOT headline restatements or verb phrases.
+- Good keywords: noun phrases or named concepts (e.g. "drone strikes", "energy infrastructure", "nuclear plant", "oil refinery").
+- Bad keywords: full headline fragments, verb-heavy phrases, or anything over 2 words.
+- Keywords should capture the *themes* of the story, not repeat entity names already in the entities list.
+- Return 2-4 keywords. Fewer is better than bad ones.
+
+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].