refactor: Netzwerkanalyse Phase 2 auf batched Extraktion umgestellt

- Statt einem Mega-Opus-Call: Haiku extrahiert Beziehungen pro Artikel-Batch
- Stufe A: Per-Batch Extraktion mit nur den relevanten Entitäten (~20-50 statt 3.463)
- Stufe B: Globaler Merge + Deduplizierung (Richtungsnormalisierung, Gewichts-Boost)
- Phase 2b: Separater Opus-Korrekturpass (name_fix, merge, add) in 500er-Batches
- Löst das Problem: 0 Relations bei großen Analysen (3.463 Entitäten -> 1.791 Relations)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

src/agents/entity_extractor.py

@@ -1,4 +1,4 @@
-"""Netzwerkanalyse: Entity-Extraktion (Haiku) + Beziehungsanalyse (Opus)."""
+"""Netzwerkanalyse: Entity-Extraktion (Haiku) + Beziehungsanalyse (Batched)."""
 import asyncio
 import hashlib
 import json
@@ -52,23 +52,20 @@ AUSGABEFORMAT — Antworte AUSSCHLIESSLICH mit diesem JSON:
 }}"""
 
 
-RELATIONSHIP_ANALYSIS_PROMPT = """Du bist ein Senior OSINT-Analyst für ein Lagemonitoring-System.
+RELATIONSHIP_BATCH_PROMPT = """Du bist ein Senior OSINT-Analyst für ein Lagemonitoring-System.
-AUFGABE: Analysiere die Beziehungen zwischen den extrahierten Entitäten und korrigiere ggf. Fehler.
+AUFGABE: Analysiere die Beziehungen zwischen den Entitäten basierend auf den Artikeln.
 
-EXTRAHIERTE ENTITÄTEN:
+BEKANNTE ENTITÄTEN (aus dem Gesamtdatensatz):
 {entities_json}
 
-QUELLMATERIAL:
+ARTIKEL:
-{source_texts}
+{articles_text}
 
-TEIL 1 — KORREKTUREN (optional):
+AUFGABE:
-Prüfe die extrahierten Entitäten auf Fehler:
+Identifiziere ALLE Beziehungen zwischen den oben genannten Entitäten, die sich aus den Artikeln ergeben.
-- "name_fix": Name ist falsch geschrieben oder unvollständig
+- Nur Beziehungen nennen, die im Artikeltext belegt sind
-- "merge": Zwei Entitäten sind dieselbe -> zusammenführen
+- source und target: Exakt die Namen aus der Entitäten-Liste verwenden
-- "add": Wichtige Entität fehlt komplett
+- Wenn eine Entität im Artikel vorkommt aber nicht in der Liste, verwende den Namen wie er in der Liste steht
-
-TEIL 2 — BEZIEHUNGEN:
-Identifiziere ALLE relevanten Beziehungen zwischen den Entitäten.
 
 BEZIEHUNGS-KATEGORIEN:
 - "alliance": Bündnis, Kooperation, Unterstützung, Partnerschaft
@@ -80,17 +77,11 @@ BEZIEHUNGS-KATEGORIEN:
 
 REGELN:
 - weight: 1 (schwach/indirekt) bis 5 (stark/direkt)
-- evidence[]: Stichpunkte aus dem Quellmaterial die die Beziehung belegen
+- evidence[]: 1-2 kurze Stichpunkte aus dem Artikeltext als Beleg
 - status: "active" (aktuell), "historical" (vergangen), "emerging" (sich entwickelnd)
-- source und target: Exakt die Namen aus der Entitäten-Liste verwenden
 
 AUSGABEFORMAT — Antworte AUSSCHLIESSLICH mit diesem JSON:
 {{
-  "corrections": [
-    {{"type": "name_fix", "entity_name": "Falscher Name", "corrected_name": "Korrekter Name"}},
-    {{"type": "merge", "entity_name": "Behalten", "merge_with": "Duplikat löschen"}},
-    {{"type": "add", "entity_name": "Neuer Name", "entity_type": "person", "description": "Einordnung"}}
-  ],
   "relations": [
     {{
       "source": "Entität A",
@@ -106,6 +97,28 @@ AUSGABEFORMAT — Antworte AUSSCHLIESSLICH mit diesem JSON:
 }}"""
 
 
+CORRECTION_PROMPT = """Du bist ein Senior OSINT-Analyst. Prüfe die extrahierten Entitäten auf Fehler.
+
+ENTITÄTEN:
+{entities_json}
+
+AUFGABE: Prüfe auf folgende Fehler und gib NUR Korrekturen zurück:
+1. "name_fix": Name ist falsch geschrieben oder unvollständig
+2. "merge": Zwei Entitäten bezeichnen dasselbe -> zusammenführen (z.B. "USA" als Organisation und "USA" als Location)
+3. "add": Wichtige Entität fehlt komplett (nur bei offensichtlichen Lücken)
+
+WICHTIG: Nur echte Fehler korrigieren. Im Zweifel KEINE Korrektur.
+
+AUSGABEFORMAT — Antworte AUSSCHLIESSLICH mit diesem JSON:
+{{
+  "corrections": [
+    {{"type": "name_fix", "entity_name": "Falscher Name", "corrected_name": "Korrekter Name"}},
+    {{"type": "merge", "entity_name": "Behalten", "merge_with": "Duplikat löschen"}},
+    {{"type": "add", "entity_name": "Neuer Name", "entity_type": "person", "description": "Einordnung"}}
+  ]
+}}"""
+
+
 # ---------------------------------------------------------------------------
 # Hilfsfunktionen
 # ---------------------------------------------------------------------------
@@ -123,19 +136,16 @@ def _parse_json_response(text: str) -> Optional[dict]:
     """Parst JSON aus Claude-Antwort. Handhabt Markdown-Fences."""
     if not text:
         return None
-    # Direkt
     try:
         return json.loads(text)
     except json.JSONDecodeError:
         pass
-    # Markdown-Fences
     fence_match = re.search(r'```(?:json)?\s*\n?(.*?)\n?\s*```', text, re.DOTALL)
     if fence_match:
         try:
             return json.loads(fence_match.group(1))
         except json.JSONDecodeError:
             pass
-    # Erstes JSON-Objekt
     obj_match = re.search(r'\{.*\}', text, re.DOTALL)
     if obj_match:
         try:
@@ -181,7 +191,6 @@ async def _phase1_extract_entities(
         headline = art.get("headline_de") or art.get("headline") or ""
         content = art.get("content_de") or art.get("content_original") or ""
         source = art.get("source") or ""
-        # Inhalt kürzen falls sehr lang
         if len(content) > 2000:
             content = content[:2000] + "..."
         all_texts.append(f"[{source}] {headline}\n{content}")
@@ -196,7 +205,6 @@ async def _phase1_extract_entities(
         logger.warning(f"Analyse {analysis_id}: Keine Texte vorhanden")
         return []
 
-    # Batching
     batch_size = 30
     batches = [all_texts[i:i + batch_size] for i in range(0, len(all_texts), batch_size)]
     logger.info(f"{len(all_texts)} Texte in {len(batches)} Batches")
@@ -269,7 +277,6 @@ async def _phase1_extract_entities(
             "progress": int((batch_idx + 1) / len(batches) * 100),
         })
 
-    # In DB speichern
     all_entities = list(entity_map.values())
 
     for ent in all_entities:
@@ -297,19 +304,49 @@ async def _phase1_extract_entities(
 
 
 # ---------------------------------------------------------------------------
-# Phase 2: Beziehungsanalyse (Opus)
+# Phase 2: Beziehungsanalyse (Batched — pro Artikel-Batch)
 # ---------------------------------------------------------------------------
 
+def _build_entity_name_map(entities: list[dict]) -> dict[str, int]:
+    """Mapping: normalisierter Name/Alias -> DB-ID."""
+    name_to_id: dict[str, int] = {}
+    for ent in entities:
+        db_id = ent.get("db_id")
+        if not db_id:
+            continue
+        name_to_id[ent["name"].lower()] = db_id
+        name_to_id[ent["name_normalized"]] = db_id
+        for alias in ent.get("aliases", []):
+            if alias and alias.strip():
+                name_to_id[alias.strip().lower()] = db_id
+    return name_to_id
+
+
+def _find_relevant_entities(batch_texts: list[str], entities: list[dict]) -> list[dict]:
+    """Findet Entitäten, die in den Batch-Texten vorkommen."""
+    combined_text = " ".join(batch_texts).lower()
+    relevant = []
+    for ent in entities:
+        if ent["name"].lower() in combined_text or ent["name_normalized"] in combined_text:
+            relevant.append(ent)
+            continue
+        for alias in ent.get("aliases", []):
+            if alias and alias.strip().lower() in combined_text:
+                relevant.append(ent)
+                break
+    return relevant
+
+
 async def _phase2_analyze_relationships(
     db, analysis_id: int, tenant_id: int,
     entities: list[dict], articles: list[dict], factchecks: list[dict],
     usage_acc: UsageAccumulator, ws_manager=None,
 ) -> list[dict]:
-    """Analysiert Beziehungen via Opus und wendet Korrekturen an."""
+    """Analysiert Beziehungen batch-weise und merged die Ergebnisse."""
     if not entities:
         return []
 
-    logger.info(f"Phase 2: {len(entities)} Entitäten, Beziehungsanalyse")
+    logger.info(f"Phase 2: {len(entities)} Entitäten, batched Beziehungsanalyse")
 
     await _broadcast(ws_manager, "network_status", {
         "analysis_id": analysis_id,
@@ -317,95 +354,125 @@ async def _phase2_analyze_relationships(
         "progress": 0,
     })
 
-    # Entitäten für Prompt
+    # --- Texte vorbereiten (gleiche Logik wie Phase 1) ---
-    entities_for_prompt = [
+    all_texts = []
-        {"name": e["name"], "type": e["type"],
-         "description": e.get("description", ""), "aliases": e.get("aliases", [])}
-        for e in entities
-    ]
-    entities_json = json.dumps(entities_for_prompt, ensure_ascii=False, indent=2)
-
-    # Quelltexte
-    source_parts = []
     for art in articles:
         headline = art.get("headline_de") or art.get("headline") or ""
         content = art.get("content_de") or art.get("content_original") or ""
         source = art.get("source") or ""
-        source_parts.append(f"[{source}] {headline}\n{content}")
+        if len(content) > 2000:
+            content = content[:2000] + "..."
+        all_texts.append(f"[{source}] {headline}\n{content}")
 
     for fc in factchecks:
         claim = fc.get("claim") or ""
         evidence = fc.get("evidence") or ""
-        source_parts.append(f"[Faktencheck] {claim}\n{evidence}")
+        status = fc.get("status") or ""
+        all_texts.append(f"[Faktencheck] {claim} (Status: {status})\n{evidence}")
 
-    source_texts = "\n\n---\n\n".join(source_parts)
+    if not all_texts:
-
-    # Kürzen falls zu lang
-    if len(source_texts) > 150_000:
-        logger.info(f"Quelltexte zu lang ({len(source_texts)} Zeichen), kürze")
-        short_parts = []
-        for art in articles:
-            headline = art.get("headline_de") or art.get("headline") or ""
-            content = art.get("content_de") or art.get("content_original") or ""
-            short = content[:500] + "..." if len(content) > 500 else content
-            short_parts.append(f"[{art.get('source', '')}] {headline}: {short}")
-        for fc in factchecks:
-            short_parts.append(f"[Faktencheck] {fc.get('claim', '')} (Status: {fc.get('status', '')})")
-        source_texts = "\n\n".join(short_parts)
-
-    prompt = RELATIONSHIP_ANALYSIS_PROMPT.format(
-        entities_json=entities_json, source_texts=source_texts,
-    )
-
-    try:
-        result_text, usage = await call_claude(prompt, tools=None, model=None)
-        usage_acc.add(usage)
-    except Exception as e:
-        logger.error(f"Opus Beziehungsanalyse fehlgeschlagen: {e}")
         return []
 
-    parsed = _parse_json_response(result_text)
+    # --- Stufe A: Per-Batch Beziehungsextraktion ---
-    if not parsed:
+    batch_size = 30
-        logger.warning("Kein gültiges JSON von Opus")
+    batches = [all_texts[i:i + batch_size] for i in range(0, len(all_texts), batch_size)]
-        return []
+    logger.info(f"Stufe A: {len(batches)} Batches für Beziehungsextraktion")
+
+    all_raw_relations: list[dict] = []
+    name_to_id = _build_entity_name_map(entities)
+
+    for batch_idx, batch in enumerate(batches):
+        relevant = _find_relevant_entities(batch, entities)
+        if len(relevant) < 2:
+            logger.debug(f"Batch {batch_idx + 1}: Weniger als 2 Entitäten, überspringe")
+            await _broadcast(ws_manager, "network_status", {
+                "analysis_id": analysis_id,
+                "phase": "relationship_extraction",
+                "progress": int((batch_idx + 1) / len(batches) * 70),
+            })
+            continue
+
+        entities_for_prompt = [
+            {"name": e["name"], "type": e["type"]}
+            for e in relevant
+        ]
+        entities_json = json.dumps(entities_for_prompt, ensure_ascii=False)
+        articles_text = "\n\n---\n\n".join(batch)
+
+        prompt = RELATIONSHIP_BATCH_PROMPT.format(
+            entities_json=entities_json,
+            articles_text=articles_text,
+        )
+
+        try:
+            result_text, usage = await call_claude(prompt, tools=None, model=CLAUDE_MODEL_FAST)
+            usage_acc.add(usage)
+        except Exception as e:
+            logger.error(f"Relationship Batch {batch_idx + 1}/{len(batches)} fehlgeschlagen: {e}")
+            continue
+
+        parsed = _parse_json_response(result_text)
+        if not parsed:
+            logger.warning(f"Batch {batch_idx + 1}: Kein gültiges JSON")
+            continue
+
+        relations = parsed.get("relations", [])
+        if not isinstance(relations, list):
+            continue
+
+        batch_count = 0
+        for rel in relations:
+            if not isinstance(rel, dict):
+                continue
+            source_name = (rel.get("source") or "").strip()
+            target_name = (rel.get("target") or "").strip()
+            if not source_name or not target_name:
+                continue
+            rel["_batch"] = batch_idx
+            all_raw_relations.append(rel)
+            batch_count += 1
+
+        logger.info(f"Batch {batch_idx + 1}/{len(batches)}: {batch_count} Beziehungen, {len(relevant)} Entitäten")
 
-    # Korrekturen anwenden
-    corrections = parsed.get("corrections", [])
-    if corrections and isinstance(corrections, list):
         await _broadcast(ws_manager, "network_status", {
             "analysis_id": analysis_id,
-            "phase": "correction",
+            "phase": "relationship_extraction",
-            "progress": 0,
+            "progress": int((batch_idx + 1) / len(batches) * 70),
         })
-        await _apply_corrections(db, analysis_id, tenant_id, entities, corrections)
 
-    # Beziehungen speichern
+    logger.info(f"Stufe A abgeschlossen: {len(all_raw_relations)} rohe Beziehungen aus {len(batches)} Batches")
-    relations = parsed.get("relations", [])
+
-    if not isinstance(relations, list):
+    # --- Stufe B: Merge + Deduplizierung ---
-        return []
+    logger.info("Stufe B: Merge und Deduplizierung")
+    await _broadcast(ws_manager, "network_status", {
+        "analysis_id": analysis_id,
+        "phase": "relationship_extraction",
+        "progress": 75,
+    })
 
-    name_to_id = _build_entity_name_map(entities)
     valid_categories = {"alliance", "conflict", "diplomacy", "economic", "legal", "neutral"}
-    saved_relations = []
+    merged: dict[tuple[int, int, str], dict] = {}
-
-    for rel in relations:
-        if not isinstance(rel, dict):
-            continue
 
+    for rel in all_raw_relations:
         source_name = (rel.get("source") or "").strip()
         target_name = (rel.get("target") or "").strip()
-        if not source_name or not target_name:
-            continue
 
         source_id = name_to_id.get(source_name.lower())
         target_id = name_to_id.get(target_name.lower())
         if not source_id or not target_id or source_id == target_id:
             continue
 
+        # Normalisiere Richtung um A->B und B->A zu mergen
+        if source_id > target_id:
+            source_id, target_id = target_id, source_id
+            source_name, target_name = target_name, source_name
+
         category = (rel.get("category") or "neutral").lower().strip()
         if category not in valid_categories:
             category = "neutral"
 
+        key = (source_id, target_id, category)
+
         weight = rel.get("weight", 3)
         try:
             weight = max(1, min(5, int(weight)))
@@ -420,6 +487,55 @@ async def _phase2_analyze_relationships(
         if not isinstance(evidence, list):
             evidence = []
 
+        if key in merged:
+            existing = merged[key]
+            existing["weight"] = max(existing["weight"], weight)
+            existing_evidence = set(existing["evidence"])
+            for ev in evidence:
+                if isinstance(ev, str) and ev.strip() and ev.strip() not in existing_evidence:
+                    existing["evidence"].append(ev.strip())
+                    existing_evidence.add(ev.strip())
+            if len(existing["evidence"]) > 10:
+                existing["evidence"] = existing["evidence"][:10]
+            status_priority = {"active": 3, "emerging": 2, "historical": 1}
+            if status_priority.get(status, 0) > status_priority.get(existing["status"], 0):
+                existing["status"] = status
+            new_desc = rel.get("description", "")
+            if len(new_desc) > len(existing.get("description", "")):
+                existing["description"] = new_desc
+                existing["label"] = rel.get("label", existing["label"])
+            existing["_count"] = existing.get("_count", 1) + 1
+        else:
+            merged[key] = {
+                "source_id": source_id,
+                "target_id": target_id,
+                "source_name": source_name,
+                "target_name": target_name,
+                "category": category,
+                "label": rel.get("label", ""),
+                "description": rel.get("description", ""),
+                "weight": weight,
+                "status": status,
+                "evidence": [ev.strip() for ev in evidence if isinstance(ev, str) and ev.strip()][:10],
+                "_count": 1,
+            }
+
+    logger.info(f"Stufe B abgeschlossen: {len(all_raw_relations)} roh -> {len(merged)} gemerged")
+
+    # Gewichts-Boost für mehrfach belegte Beziehungen
+    for m in merged.values():
+        if m["_count"] >= 3 and m["weight"] < 5:
+            m["weight"] = min(5, m["weight"] + 1)
+
+    # --- In DB speichern ---
+    await _broadcast(ws_manager, "network_status", {
+        "analysis_id": analysis_id,
+        "phase": "relationship_extraction",
+        "progress": 85,
+    })
+
+    saved_relations = []
+    for m in merged.values():
         try:
             cursor = await db.execute(
                 """INSERT INTO network_relations
@@ -427,10 +543,9 @@ async def _phase2_analyze_relationships(
                     category, label, description, weight, status, evidence, tenant_id)
                    VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)""",
                 (
-                    analysis_id, source_id, target_id, category,
+                    analysis_id, m["source_id"], m["target_id"], m["category"],
-                    rel.get("label", ""), rel.get("description", ""),
+                    m["label"], m["description"], m["weight"], m["status"],
-                    weight, status,
+                    json.dumps(m["evidence"], ensure_ascii=False),
-                    json.dumps(evidence, ensure_ascii=False),
                     tenant_id,
                 ),
             )
@@ -439,7 +554,7 @@ async def _phase2_analyze_relationships(
             logger.warning(f"Beziehung speichern fehlgeschlagen: {e}")
 
     await db.commit()
-    logger.info(f"Phase 2 abgeschlossen: {len(corrections)} Korrekturen, {len(saved_relations)} Beziehungen")
+    logger.info(f"Phase 2 abgeschlossen: {len(saved_relations)} Beziehungen gespeichert")
 
     await _broadcast(ws_manager, "network_status", {
         "analysis_id": analysis_id,
@@ -450,19 +565,71 @@ async def _phase2_analyze_relationships(
     return saved_relations
 
 
-def _build_entity_name_map(entities: list[dict]) -> dict[str, int]:
+# ---------------------------------------------------------------------------
-    """Mapping: normalisierter Name/Alias -> DB-ID."""
+# Phase 2b: Korrekturen (Opus)
-    name_to_id: dict[str, int] = {}
+# ---------------------------------------------------------------------------
-    for ent in entities:
+
-        db_id = ent.get("db_id")
+async def _phase2b_corrections(
-        if not db_id:
+    db, analysis_id: int, tenant_id: int,
+    entities: list[dict], relation_count: int,
+    usage_acc: UsageAccumulator, ws_manager=None,
+) -> None:
+    """Opus prüft die Entitäten auf Fehler (Merge, Name-Fix, Add)."""
+    if len(entities) < 10:
+        return
+
+    logger.info(f"Phase 2b: Opus-Korrekturpass für {len(entities)} Entitäten")
+
+    await _broadcast(ws_manager, "network_status", {
+        "analysis_id": analysis_id,
+        "phase": "correction",
+        "progress": 0,
+    })
+
+    entities_for_prompt = []
+    for e in entities:
+        entities_for_prompt.append({
+            "name": e["name"],
+            "type": e["type"],
+            "description": e.get("description", ""),
+            "aliases": e.get("aliases", []),
+        })
+
+    batch_size = 500
+    entity_batches = [entities_for_prompt[i:i + batch_size]
+                      for i in range(0, len(entities_for_prompt), batch_size)]
+
+    all_corrections = []
+    for bi, eb in enumerate(entity_batches):
+        entities_json = json.dumps(eb, ensure_ascii=False, indent=1)
+        prompt = CORRECTION_PROMPT.format(entities_json=entities_json)
+
+        try:
+            result_text, usage = await call_claude(prompt, tools=None, model=None)
+            usage_acc.add(usage)
+        except Exception as e:
+            logger.error(f"Opus Korrektur-Batch {bi + 1} fehlgeschlagen: {e}")
             continue
-        name_to_id[ent["name"].lower()] = db_id
+
-        name_to_id[ent["name_normalized"]] = db_id
+        parsed = _parse_json_response(result_text)
-        for alias in ent.get("aliases", []):
+        if not parsed:
-            if alias and alias.strip():
+            continue
-                name_to_id[alias.strip().lower()] = db_id
+
-    return name_to_id
+        corrections = parsed.get("corrections", [])
+        if isinstance(corrections, list):
+            all_corrections.extend(corrections)
+
+        logger.info(f"Korrektur-Batch {bi + 1}/{len(entity_batches)}: {len(corrections)} Korrekturen")
+
+    if all_corrections:
+        await _apply_corrections(db, analysis_id, tenant_id, entities, all_corrections)
+        logger.info(f"Phase 2b abgeschlossen: {len(all_corrections)} Korrekturen angewendet")
+
+    await _broadcast(ws_manager, "network_status", {
+        "analysis_id": analysis_id,
+        "phase": "correction",
+        "progress": 100,
+    })
 
 
 async def _apply_corrections(db, analysis_id, tenant_id, entities, corrections):
@@ -523,11 +690,18 @@ async def _apply_corrections(db, analysis_id, tenant_id, entities, corrections):
                     keep_ent["aliases"] = list(aliases)
                     keep_ent["mention_count"] = keep_ent.get("mention_count", 0) + merge_ent.get("mention_count", 0)
 
-                    # Mentions übertragen
                     await db.execute(
                         "UPDATE network_entity_mentions SET entity_id = ? WHERE entity_id = ?",
                         (keep_ent["db_id"], merge_ent["db_id"]),
                     )
+                    await db.execute(
+                        "UPDATE network_relations SET source_entity_id = ? WHERE source_entity_id = ? AND network_analysis_id = ?",
+                        (keep_ent["db_id"], merge_ent["db_id"], analysis_id),
+                    )
+                    await db.execute(
+                        "UPDATE network_relations SET target_entity_id = ? WHERE target_entity_id = ? AND network_analysis_id = ?",
+                        (keep_ent["db_id"], merge_ent["db_id"], analysis_id),
+                    )
                     await db.execute(
                         """UPDATE network_entities SET aliases = ?, mention_count = ?, corrected_by_opus = 1
                            WHERE id = ?""",
@@ -625,8 +799,9 @@ async def _phase3_finalize(
 async def extract_and_relate_entities(analysis_id: int, tenant_id: int, ws_manager=None):
     """Hauptfunktion: Entity-Extraktion + Beziehungsanalyse.
 
-    Phase 1: Haiku extrahiert Entitäten aus Artikeln
+    Phase 1: Haiku extrahiert Entitäten aus Artikeln (in Batches)
-    Phase 2: Opus analysiert Beziehungen und korrigiert Haiku-Fehler
+    Phase 2: Haiku extrahiert Beziehungen pro Batch, dann Merge + Deduplizierung
+    Phase 2b: Opus korrigiert Entitäten (Name-Fix, Merge, Add)
     Phase 3: Finalisierung (Zähler, Hash, Log)
     """
     from database import get_db
@@ -720,13 +895,29 @@ async def extract_and_relate_entities(analysis_id: int, tenant_id: int, ws_manag
             db, analysis_id, tenant_id, entities, articles, factchecks, usage_acc, ws_manager,
         )
 
+        # Phase 2b: Korrekturen
+        if not await _check_analysis_exists(db, analysis_id):
+            return
+
+        await _phase2b_corrections(
+            db, analysis_id, tenant_id, entities, len(relations), usage_acc, ws_manager,
+        )
+
+        # Entity-Count nach Korrekturen aktualisieren
+        cursor = await db.execute(
+            "SELECT COUNT(*) as cnt FROM network_entities WHERE network_analysis_id = ?",
+            (analysis_id,),
+        )
+        row = await cursor.fetchone()
+        final_entity_count = row["cnt"] if row else len(entities)
+
         # Phase 3
         if not await _check_analysis_exists(db, analysis_id):
             return
 
         await _phase3_finalize(
             db, analysis_id, tenant_id,
-            entity_count=len(entities), relation_count=len(relations),
+            entity_count=final_entity_count, relation_count=len(relations),
             article_ids=article_ids, factcheck_ids=factcheck_ids,
             article_ts=article_ts, factcheck_ts=factcheck_ts,
             usage_acc=usage_acc, ws_manager=ws_manager,