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.
- 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.
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"] }

INPUT:
{cluster_json}