feat(control-pipeline): persist SGE Knowledge Compiler (capability execution engine)

Move the proven, behavior-equivalent SGE compiler from the macmini build env into
the repo (release hygiene, no behavior change, no new capability/architecture):
- scripts/sge_build.py — structured guidance extractor + capability execution
  engine. main() runs run_engine(): C6 Tables -> C7 ReadingOrder -> C1/C2 Sections
  -> C4 References, order derived topologically from each capability consume/produce
  contract (not hardcoded). Region ownership: C6 claims tables, C7 residual prose.
- scripts/capability_pipeline.py — Region IR {id,bbox,type,state,owner},
  claim/consume/produce capabilities, topological resolve_order().
- scripts/reading_order.py — C7 reading-order reconstruction (multi-column reflow;
  identity gate on single-column so output==input).

Verified bit-identical: artifact graph (IDs, parent/child, metadata, text) unchanged
vs the pre-engine direct path across 8 docs (4 layout families x EN/DE,
0 mismatch / 0 only_base / 0 only_engine); Golden degraded=0. BUILD_CP default is now
__file__-relative so the script self-locates control-pipeline/services.

control-pipeline/scripts/capability_pipeline.py

@@ -0,0 +1,92 @@
+"""Capability Execution Engine (Prototyp). Region-IR mit Runtime-Owner; Capabilities
+deklarieren claims/consumes/produces; Ausführungsreihenfolge wird aus dem Artefakt-Graphen
+ABGELEITET (topologisch), nicht hartkodiert. Realisiert C6/C7 als Pipeline-Stages mit Region-Ownership."""
+from dataclasses import dataclass, field
+import reading_order as RO
+
+@dataclass
+class Region:
+    id:int; bbox:tuple; type:str="unknown"; state:str="unclaimed"; owner:str=None
+
+@dataclass
+class Artifact:
+    kind:str; payload:object; source_region:int=None
+
+class Capability:
+    name=""; consumes=[]; produces=[]; residual=False
+    def claims(self, region): return False
+    def run(self, regions, page, artifacts): return []
+
+class TableExtraction(Capability):
+    name="C6_TableExtraction"; consumes=["table_region"]; produces=["table_units"]
+    def claims(self, region): return region.type=="table"
+    def run(self, regions, page, artifacts):
+        out=[]
+        for r in regions:
+            crop=page.crop(_clamp(r.bbox, page))
+            tbls=crop.find_tables()
+            rows=tbls[0].extract() if tbls else []
+            out.append(Artifact("table_units", {"region":r.id,"rows":len(rows)}, r.id))
+        return out
+
+class ReadingOrder(Capability):
+    name="C7_ReadingOrder"; consumes=["prose_region"]; produces=["ordered_prose"]; residual=True
+    def claims(self, region): return region.type=="prose"
+    def run(self, regions, page, artifacts):
+        table_bboxes=[a.payload for a in artifacts if a.kind=="_table_bbox"]
+        ws=[w for w in page.extract_words() if not _in_any(w, table_bboxes)]
+        text=RO.emit_words(ws, float(page.width)) if hasattr(RO,"emit_words") else ""
+        return [Artifact("ordered_prose", {"words":len(ws),"chars":len(text)}, None)]
+
+class FigureExtraction(Capability):
+    name="C8_FigureExtraction"; consumes=["figure_region"]; produces=["figure_units"]
+    def claims(self, region): return region.type=="figure"
+
+class References(Capability):
+    name="C4_References"; consumes=["ordered_prose"]; produces=["citation_units"]
+
+def _in_any(w, bboxes):
+    cx=(w["x0"]+w["x1"])/2; cy=(w["top"]+w["bottom"])/2
+    for (x0,t,x1,b) in bboxes:
+        if x0<=cx<=x1 and t<=cy<=b: return True
+    return False
+
+def _clamp(b, page):
+    x0,t,x1,bt=b
+    return (max(0,x0),max(0,t),min(float(page.width),x1),min(float(page.height),bt))
+
+def segment(page):
+    regions=[]; rid=0; tbb=[]; W=float(page.width); H=float(page.height)
+    for t in page.find_tables():
+        b=_clamp(t.bbox,page)
+        if (b[2]-b[0])>=0.25*W and (b[3]-b[1])>=25:   # substanzielle Tabelle, keine Footer-Artefakte
+            regions.append(Region(rid,b,"table")); tbb.append(b); rid+=1
+    regions.append(Region(rid,(0,0,W,H),"prose")); rid+=1
+    return regions, tbb
+
+def resolve_order(caps, raw_types):
+    available=set(raw_types); ordered=[]; remaining=list(caps)
+    while remaining:
+        progressed=False
+        for c in list(remaining):
+            if all(dep in available for dep in c.consumes):
+                ordered.append(c); available.update(c.produces); remaining.remove(c); progressed=True
+        if not progressed: raise RuntimeError("unsatisfiable: "+str([c.name for c in remaining]))
+    return ordered
+
+def run_pipeline(page, caps):
+    regions, tbb=segment(page)
+    raw_types=set(r.type+"_region" for r in regions)
+    order=resolve_order(caps, raw_types)
+    artifacts=[Artifact("_table_bbox",b) for b in tbb]   # geometrie für C7-Ausschluss
+    # CLAIM-Phase: spezifische Claimer zuerst, residual zuletzt
+    for c in sorted(order, key=lambda c:c.residual):
+        for r in regions:
+            if r.state=="unclaimed" and c.claims(r):
+                r.state="claimed"; r.owner=c.name
+    # RUN-Phase in abgeleiteter Reihenfolge
+    for c in order:
+        owned=[r for r in regions if r.owner==c.name]
+        if owned or any(dep=="ordered_prose" for dep in c.consumes):
+            artifacts+= c.run(owned, page, artifacts)
+    return regions, [a for a in artifacts if not a.kind.startswith("_")], order

control-pipeline/scripts/reading_order.py

@@ -0,0 +1,107 @@
+"""C7 Reading Order Reconstruction (Pilot). ReadingRegion-Modell, Identity-Gate.
+Scope: NUR Detect Regions / Determine Order / Emit Linear. KEINE Tabellen/Bilder/Sidebars/Fussnoten/Callouts."""
+import statistics
+
+def _lines(words, ytol=3.0):
+    ws=sorted(words,key=lambda w:(round(w["top"],1),w["x0"])); lines=[]; cur=[]; cy=None
+    for w in ws:
+        if cy is None or abs(w["top"]-cy)<=ytol: cur.append(w); cy=w["top"] if cy is None else cy
+        else: lines.append(cur); cur=[w]; cy=w["top"]
+    if cur: lines.append(cur)
+    return lines
+
+def _gutters(words, W):
+    G=160; xs=[W*i/G for i in range(G+1)]
+    cov=[sum(1 for w in words if w["x0"]<=x<=w["x1"]) for x in xs]
+    pos=[c for c in cov if c>0]
+    if not pos: return []
+    body=statistics.median(pos)
+    if body<6: return []
+    thr=max(1,0.15*body); gut=[]; i=0
+    while i<=G:
+        if cov[i]<thr and 0.15*W<=xs[i]<=0.85*W:
+            j=i
+            while j<=G and cov[j]<thr: j+=1
+            if xs[min(j,G)]-xs[i]>=0.02*W: gut.append((xs[i]+xs[min(j-1,G)])/2)
+            i=j
+        else: i+=1
+    return gut
+
+def detect_regions(pg):
+    ws=pg.extract_words(); W=float(pg.width)
+    if len(ws)<60: return {"type":"single","reason":"sparse"}, ws
+    cuts=_gutters(ws,W)
+    if not cuts: return {"type":"single","reason":"no-gutter"}, ws
+    def rc(a,b): return sum(1 for w in ws if a<=(w["x0"]+w["x1"])/2<b)
+    minw=max(25,0.12*len(ws))
+    keep=list(cuts); changed=True
+    while keep and changed:
+        changed=False; bnds=[0]+keep+[W]
+        cnt=[rc(bnds[i],bnds[i+1]) for i in range(len(bnds)-1)]
+        mn=min(range(len(cnt)),key=lambda i:cnt[i])
+        if cnt[mn]<minw:
+            if mn==0: del keep[0]
+            elif mn==len(cnt)-1: del keep[-1]
+            elif cnt[mn-1]<=cnt[mn+1]: del keep[mn-1]
+            else: del keep[mn]
+            changed=True
+    if not keep: return {"type":"single","reason":"thin-merged"}, ws
+    bounds=[0]+keep+[W]; cols=[(bounds[k],bounds[k+1]) for k in range(len(bounds)-1)]
+    return {"type":"multi","cols":cols,"cuts":keep,"ncols":len(cols)}, ws
+
+def emit_linear(pg):
+    info,ws=detect_regions(pg)
+    if info["type"]=="single": return pg.extract_text() or ""
+    cuts=info["cuts"]; cols=info["cols"]; W=float(pg.width)
+    def colidx(x):
+        for k,c in enumerate(cols):
+            if c[0]<=x<c[1]: return k
+        return len(cols)-1
+    seq=[]
+    for ln in _lines(ws):
+        sw=sorted(ln,key=lambda w:w["x0"]); frags=[[sw[0]]]
+        for i in range(1,len(sw)):
+            if any(sw[i-1]["x1"]<=c<=sw[i]["x0"] for c in cuts): frags.append([sw[i]])
+            else: frags[-1].append(sw[i])
+        for fr in frags:
+            x0=min(w["x0"] for w in fr); x1=max(w["x1"] for w in fr); top=min(w["top"] for w in fr)
+            text=" ".join(w["text"] for w in fr); spans=sum(1 for c in cuts if x0<c<x1)
+            seq.append(("full",None,top,text) if spans>=1 else ("col",colidx((x0+x1)/2),top,text))
+    out=[]; buf=[]
+    def flush(b):
+        res=[]
+        for k in sorted(set(x[1] for x in b)):
+            for x in sorted([x for x in b if x[1]==k], key=lambda x:x[2]): res.append(x[3])
+        return res
+    for it in seq:
+        if it[0]=="full":
+            if buf: out+=flush(buf); buf=[]
+            out.append(it[3])
+        else: buf.append(it)
+    if buf: out+=flush(buf)
+    return "\n".join(out)
+
+
+def emit_words(ws, W):
+    flat=lambda L: " ".join(w["text"] for w in sorted(L,key=lambda w:(round(w["top"],1),w["x0"])))
+    if len(ws)<60: return flat(ws)
+    cuts=_gutters(ws,W)
+    if not cuts: return flat(ws)
+    bounds=[0]+cuts+[W]; cols=[(bounds[k],bounds[k+1]) for k in range(len(cuts)+1)]
+    def colidx(x):
+        for k,c in enumerate(cols):
+            if c[0]<=x<c[1]: return k
+        return len(cols)-1
+    buf={}
+    for ln in _lines(ws):
+        sw=sorted(ln,key=lambda w:w["x0"]); frags=[[sw[0]]]
+        for i in range(1,len(sw)):
+            if any(sw[i-1]["x1"]<=c<=sw[i]["x0"] for c in cuts): frags.append([sw[i]])
+            else: frags[-1].append(sw[i])
+        for fr in frags:
+            mid=(min(w["x0"] for w in fr)+max(w["x1"] for w in fr))/2
+            buf.setdefault(colidx(mid),[]).append((min(w["top"] for w in fr)," ".join(w["text"] for w in fr)))
+    out=[]
+    for k in sorted(buf):
+        for top,t in sorted(buf[k]): out.append(t)
+    return chr(10).join(out)

control-pipeline/scripts/sge_build.py

@@ -0,0 +1,755 @@
+#!/usr/bin/env python3
+"""Structured Guidance Extractor — GENERIC builder (Wave 1b compiler).
+
+One deterministic parser, parametrized per document + language.
+  python3 sge_build.py --doc EDPB_WP248_DPIA --lang de [--dry-run]
+
+Source per (doc,lang) = {"pdf_url"} OR {"zip_url","inner"[, "local"]}.
+Layout parser identical to the WP243 pilot (pdfplumber MIT, NO LLM). Mehrsprachigkeit
+= representation property: SAME document_id, language=<lang>, own document_version
+namespace (no point-id collision). Emits chunks/page + refs/chunk metrics.
+"""
+import argparse, datetime, io, logging, os, re, subprocess, sys, time, zipfile
+from collections import Counter
+
+CP = os.getenv("BUILD_CP") or os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, CP)
+import httpx  # noqa: E402
+import pdfplumber  # noqa: E402
+from services.legal_act_ingester import UploadUnit, upload_unit  # noqa: E402
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
+log = logging.getLogger("sge")
+
+RAG_URL = os.getenv("RAG_URL", "https://localhost:8097")
+BUILD_COLLECTION = "bp_compliance_kb_2026_1_build"
+MANIFEST_VERSION = "regulatory_build_manifest_v3/2026.1"
+
+# --- Layout-Familien (Phase 2): detected family-String -> F-Nummer + Familienkarte (erwartete Familie je Quelle) ---
+FAMILY_F = {"arabic-hierarchical": "F1", "roman-hierarchical": "F2",
+            "arabic-caps+paragraph": "F3", "unnumbered-toc": "F4"}
+EXPECTED_FAMILY = {  # Familienkarte: VOR-Build-Erwartung, beim Build gegen detected gegated (FAMILY-GATE)
+    "EDPB_WP243_DPO": "F1", "EDPB_WP248_DPIA": "F2", "EDPB_GL_05_2020_CONSENT": "F3",
+    "EDPB_GL_09_2022_BREACH_NOTIFICATION": "F2", "EDPB_GL_07_2020_CONTROLLER_PROCESSOR": "F3",
+    "EDPB_WP260_TRANSPARENCY": "F4",
+    "DSK_SDM": "F1", "DSK_OH_KI_2024": "F1", "DSK_OH_CLOUD_2014": "F1",
+    "DSK_KP_05_DSFA": "F1",  # FAMILY-GATE-Korrektur: parst 13 Sekt. via Heuristik, kein edge-short
+    "DSK_KP_12_DSB": "edge-short", "DSK_KP_13_AV": "edge-short",  # echte Kurz-Flyer (0 Sekt., gate=FAIL)
+    # verbleibende EDPB (vorab klassifiziert = reasoned, beim Build verifiziert):
+    "EDPB_GL_01_2021_BREACH_EXAMPLES": "F3", "EDPB_GL_02_2019_ART6_1B_ONLINE": "F3",
+    "EDPB_REC_01_2020_SUPPL_MEASURES": "F3", "EDPB_GL_05_2021_ART3_CHAPTERV": "F3",
+    # ENISA-Charge (3. Herausgeber): erwartete HEADING-Familie (F1-F4); Capabilities (Tabellen/Mehrspaltig) orthogonal
+    "ENISA_HANDBOOK_PDP": "F1", "ENISA_GL_SME_PDP": "F1", "ENISA_GL_EECC_SEC": "F3",
+    "ENISA_ISPS_SME": "F1", "ENISA_TL_2023": "?",  # TL = Capability-Boundary-Kandidat (mehrspaltig/visuell)
+    # NIST (US, Public Domain) — 4. Herausgeber, doc_type=Technical Standard (refs=section/control), tabellenlastig
+    "NIST_SP_800_53B": "F1", "NIST_SP_800_171": "F1",
+}
+
+# --- Manifest-Vertrag: document_type -> expected_reference_types (User 2026-06-28: DEKLARATIV, kein Parser-Bug).
+# Der Validator verlangt Artikel-Referenzen NUR, wenn der doc_type sie erwartet -> ENISA/NIST (Standards) scheitern nicht mehr.
+ISSUER_DOCTYPE = {"Article 29 WP / EDPB": "Guidance", "EDPB": "Guidance", "DSK": "Guidance",
+                  "ENISA": "Technical Standard", "NIST": "Technical Standard"}
+DOC_TYPE_REFS = {"EU Regulation": ["article", "recital", "annex"], "German Law": ["paragraph"],
+                 "Guidance": ["article", "guidance"], "Technical Standard": ["section", "control"],
+                 "Threat Report": ["cve", "cwe", "attack"], "Whitepaper": ["bibliography"]}
+
+# --- per-document registry (resolved sources pinned) ---
+NS = "https://ec.europa.eu/newsroom/just/document.cfm?doc_id="
+DOCS = {
+    "EDPB_WP243_DPO": {  # Pilot (regression reference) — arabisch-Schema
+        "reg": "EDPB WP243", "issuer": "Article 29 WP / EDPB", "expected_sections": 9,
+        "name": "EDPB/WP29 Guidelines on Data Protection Officers (DPOs), WP 243 rev.01",
+        "sources": {
+            "en": {"pdf_url": NS + "44100"},
+            "de": {"zip_url": NS + "48137", "inner": "wp243rev01_de.pdf", "local": "/tmp/doc_48137.bin"},
+        },
+    },
+    "EDPB_WP248_DPIA": {
+        "reg": "EDPB WP248", "issuer": "Article 29 WP / EDPB", "expected_sections": 8,
+        "name": "EDPB/WP29 Guidelines on Data Protection Impact Assessment (DPIA), WP 248 rev.01",
+        "sources": {
+            "en": {"pdf_url": NS + "47711"},
+            "de": {"zip_url": NS + "48464", "inner": "wp248 rev.01_de.pdf", "local": "/tmp/wp248_48464.bin"},
+        },
+    },
+    "EDPB_GL_05_2020_CONSENT": {
+        "reg": "EDPB GL 05/2020", "issuer": "EDPB", "expected_sections": 7,
+        "name": "EDPB Guidelines 05/2020 on consent under Regulation 2016/679 (v1.1)",
+        "sources": {
+            "en": {"pdf_url": "https://www.edpb.europa.eu/system/files/documents/files/file1/edpb_guidelines_202005_consent_en.pdf"},
+            "de": {"pdf_url": "https://www.edpb.europa.eu/system/files/documents/files/file1/edpb_guidelines_202005_consent_de.pdf"},
+        },
+    },
+    "EDPB_GL_09_2022_BREACH_NOTIFICATION": {
+        "reg": "EDPB GL 09/2022", "issuer": "EDPB", "expected_sections": 6,
+        "name": "EDPB Guidelines 9/2022 on personal data breach notification under GDPR (v2.0)",
+        "sources": {
+            "en": {"pdf_url": "https://www.edpb.europa.eu/system/files/2023-04/edpb_guidelines_202209_personal_data_breach_notification_v2.0_en.pdf"},
+            "de": {"pdf_url": "https://www.edpb.europa.eu/system/files/2024-10/edpb_guidelines_202209_personal_data_breach_notification_v2.0_de_0.pdf"},
+        },
+    },
+    "EDPB_GL_07_2020_CONTROLLER_PROCESSOR": {
+        "reg": "EDPB GL 07/2020", "issuer": "EDPB", "expected_sections": 5,
+        "name": "EDPB Guidelines 07/2020 on the concepts of controller and processor in the GDPR (v2.0)",
+        "sources": {
+            "en": {"pdf_url": "https://www.edpb.europa.eu/system/files/2023-10/EDPB_guidelines_202007_controllerprocessor_final_en.pdf"},
+            "de": {"pdf_url": "https://www.edpb.europa.eu/system/files/2023-10/EDPB_guidelines_202007_controllerprocessor_final_de.pdf"},
+        },
+    },
+    "EDPB_WP260_TRANSPARENCY": {
+        "reg": "EDPB WP260", "issuer": "Article 29 WP / EDPB", "expected_sections": 5,
+        "name": "EDPB/WP29 Guidelines on transparency under Regulation 2016/679, WP 260 rev.01",
+        "sources": {
+            "en": {"pdf_url": "https://www.edpb.europa.eu/system/files/documents/2023-09/wp260rev01_en.pdf"},
+            "de": {"pdf_url": "https://www.edpb.europa.eu/system/files/documents/2023-09/wp260rev01_de.pdf"},
+        },
+    },
+    "EDPB_GL_01_2021_BREACH_EXAMPLES": {
+        "reg": "EDPB GL 01/2021", "issuer": "EDPB", "expected_sections": 4,
+        "name": "EDPB Guidelines 01/2021 on Examples regarding Personal Data Breach Notification",
+        "sources": {
+            "en": {"pdf_url": "https://www.edpb.europa.eu/system/files/documents/2022-01/edpb_guidelines_012021_pdbnotification_adopted_en.pdf"},
+            "de": {"pdf_url": "https://www.edpb.europa.eu/system/files/2022-04/edpb_guidelines_012021_pdbnotification_adopted_de.pdf"},
+        },
+    },
+    "EDPB_GL_02_2019_ART6_1B_ONLINE": {
+        "reg": "EDPB GL 02/2019", "issuer": "EDPB", "expected_sections": 4,
+        "name": "EDPB Guidelines 2/2019 on processing under Article 6(1)(b) GDPR (online services)",
+        "sources": {
+            "en": {"pdf_url": "https://www.edpb.europa.eu/sites/default/files/files/file1/edpb_guidelines-art_6-1-b-adopted_after_public_consultation_en.pdf"},
+            "de": {"pdf_url": "https://www.edpb.europa.eu/sites/default/files/files/file1/edpb_guidelines-art_6-1-b-adopted_after_public_consultation_de_0.pdf"},
+        },
+    },
+    "EDPB_REC_01_2020_SUPPL_MEASURES": {
+        "reg": "EDPB REC 01/2020", "issuer": "EDPB", "expected_sections": 4,
+        "name": "EDPB Recommendations 01/2020 on supplementary measures for transfer tools",
+        "sources": {
+            "en": {"pdf_url": "https://www.edpb.europa.eu/system/files/2021-06/edpb_recommendations_202001vo.2.0_supplementarymeasurestransferstools_en.pdf"},
+            "de": {"pdf_url": "https://www.edpb.europa.eu/system/files/2022-04/edpb_recommendations_202001vo.2.0_supplementarymeasurestransferstools_de.pdf"},
+        },
+    },
+    "EDPB_GL_05_2021_ART3_CHAPTERV": {
+        "reg": "EDPB GL 05/2021", "issuer": "EDPB", "expected_sections": 4,
+        "name": "EDPB Guidelines 05/2021 on the Interplay of Article 3 and Chapter V GDPR",
+        "sources": {
+            "en": {"pdf_url": "https://www.edpb.europa.eu/system/files/2023-02/edpb_guidelines_05-2021_interplay_between_the_application_of_art3-chapter_v_of_the_gdpr_v2_en_0.pdf"},
+            "de": {"pdf_url": "https://www.edpb.europa.eu/system/files/2023-09/edpb_guidelines_05-2021_interplay_between_the_application_de.pdf"},
+        },
+    },
+    "DSK_SDM": {  # F4-Cross-Issuer-Kandidat (DSK, dt.)
+        "reg": "DSK SDM", "issuer": "DSK", "expected_sections": 3,
+        "name": "DSK Standard-Datenschutzmodell V3.1",
+        "sources": {"de": {"pdf_url": "https://www.datenschutzkonferenz-online.de/media/ah/SDM-Methode-V31.pdf"}},
+    },
+    "DSK_OH_KI_2024": {
+        "reg": "DSK OH KI", "issuer": "DSK", "expected_sections": 3,
+        "name": "DSK Orientierungshilfe KI und Datenschutz (2024)",
+        "sources": {"de": {"pdf_url": "https://www.datenschutzkonferenz-online.de/media/oh/20240506_DSK_Orientierungshilfe_KI_und_Datenschutz.pdf"}},
+    },
+    "DSK_KP_12_DSB": {
+        "reg": "DSK KP12", "issuer": "DSK", "expected_sections": 2,
+        "name": "DSK Kurzpapier Nr. 12 - Datenschutzbeauftragte",
+        "sources": {"de": {"pdf_url": "https://www.datenschutzkonferenz-online.de/media/kp/dsk_kpnr_12.pdf"}},
+    },
+    "DSK_OH_CLOUD_2014": {
+        "reg": "DSK OH Cloud", "issuer": "DSK", "expected_sections": 3,
+        "name": "DSK Orientierungshilfe - Cloud Computing (2014)",
+        "sources": {"de": {"pdf_url": "https://www.datenschutzkonferenz-online.de/media/oh/20141009_oh_cloud_computing.pdf"}},
+    },
+    "DSK_KP_05_DSFA": {
+        "reg": "DSK KP05", "issuer": "DSK", "expected_sections": 2,
+        "name": "DSK Kurzpapier Nr. 5 - Datenschutz-Folgenabschätzung",
+        "sources": {"de": {"pdf_url": "https://www.datenschutzkonferenz-online.de/media/kp/dsk_kpnr_5.pdf"}},
+    },
+    "DSK_KP_13_AV": {
+        "reg": "DSK KP13", "issuer": "DSK", "expected_sections": 2,
+        "name": "DSK Kurzpapier Nr. 13 - Auftragsverarbeitung",
+        "sources": {"de": {"pdf_url": "https://www.datenschutzkonferenz-online.de/media/kp/dsk_kpnr_13.pdf"}},
+    },
+    "ENISA_HANDBOOK_PDP": {  # Stufe 1 — bekannte Welt (Kalibrierung)
+        "reg": "ENISA Handbook PDP", "issuer": "ENISA", "expected_sections": 4,
+        "name": "ENISA Handbook on Security of Personal Data Processing",
+        "sources": {"en": {"pdf_url": "https://www.enisa.europa.eu/sites/default/files/publications/WP2017%20O-2-2-5%20GDPR%20Measures%20Handbook.pdf"}},
+    },
+    "ENISA_GL_SME_PDP": {  # Stufe 1
+        "reg": "ENISA GL SME PDP", "issuer": "ENISA", "expected_sections": 4,
+        "name": "ENISA Guidelines for SMEs on the security of personal data processing",
+        "sources": {"en": {"pdf_url": "https://www.enisa.europa.eu/sites/default/files/publications/WP2016%203-2%206%20Data%20Controllers%20Risk.pdf"}},
+    },
+    "ENISA_GL_EECC_SEC": {  # Stufe 2 — bekannte Welt + Annex/Tabellen
+        "reg": "ENISA GL EECC", "issuer": "ENISA", "expected_sections": 4,
+        "name": "ENISA Guideline on Security Measures under the EECC (4th edition)",
+        "sources": {"en": {"pdf_url": "https://www.enisa.europa.eu/sites/default/files/publications/ENISA%20-%20Guideline%20on%20Security%20Measures%20under%20the%20EECC-%204th%20edition.pdf"}},
+    },
+    "ENISA_ISPS_SME": {  # Stufe 3 — Grenztest (tabellenzentriert)
+        "reg": "ENISA ISPS SME", "issuer": "ENISA", "expected_sections": 3,
+        "name": "ENISA Information security and privacy standards for SMEs",
+        "sources": {"en": {"pdf_url": "https://www.enisa.europa.eu/sites/default/files/publications/Information%20security%20and%20privacy%20standards%20for%20SMEs.pdf"}},
+    },
+    "ENISA_TL_2023": {  # Stufe 3 — Grenztest (mehrspaltig/visuell)
+        "reg": "ENISA TL 2023", "issuer": "ENISA", "expected_sections": 3,
+        "name": "ENISA Threat Landscape 2023",
+        "sources": {"en": {"pdf_url": "https://www.enisa.europa.eu/sites/default/files/publications/ENISA%20Threat%20Landscape%202023.pdf"}},
+    },
+    "NIST_SP_800_53B": {  # NIST (US Public Domain) — tabellenzentriert (Control Baselines)
+        "reg": "NIST SP 800-53B", "issuer": "NIST", "expected_sections": 3,
+        "name": "NIST SP 800-53B Control Baselines for Information Systems and Organizations",
+        "sources": {"en": {"pdf_url": "https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-53B.pdf"}},
+    },
+    "NIST_SP_800_171": {  # NIST — strukturierte Guidance + Tabellen
+        "reg": "NIST SP 800-171", "issuer": "NIST", "expected_sections": 3,
+        "name": "NIST SP 800-171 Rev 2 Protecting Controlled Unclassified Information",
+        "sources": {"en": {"pdf_url": "https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.800-171r2.pdf"}},
+    },
+}
+
+# --- language-specific lexical config ---
+LANG = {
+    "en": {"artref": re.compile(r'(?:Article|Art\.)\s*(\d+)'),
+           "noise": re.compile(r'ARTICLE 29 DATA PROTECTION|^\d{1,3}$|Adopted on|Revised and Adopted|Version \d', re.I)},
+    "de": {"artref": re.compile(r'(?:Artikel|Art\.)\s*(\d+)'),
+           "noise": re.compile(r'ARTIKEL.?29.?DATENSCHUTZ|ARTICLE 29 DATA PROTECTION|^\d{1,3}$|Angenommen|berarbeitet und|Adopted on|Fassung \d', re.I)},
+}
+# Generic enumerator: arabic (1 / 1.2.3) | roman (I. / III.B.) | Annex N.
+# \d{1,3} excludes years (4 digits) as section numbers. Roman requires trailing dot.
+ROMAN = r'(?:XVIII|VIII|XIII|XVII|III|VII|XII|XIV|XVI|XIX|II|IV|VI|IX|XI|XV|XX|I|V|X)'
+ENUM = re.compile(
+    r'^(?:'
+    r'(?P<ar>\d{1,3}(?:\.\d+){0,3})\.?'      # arabic 1 / 1.2.3 (self-pathed)
+    r'|(?P<ro>' + ROMAN + r')\.'             # roman top-level I. / III. (require dot)
+    r'|(?P<le>[A-Z])\.'                       # capital-letter sub A./B. (only under roman scheme)
+    r'|(?P<ax>(?:Annex|Annexe|Anhang|Anlage)\s+\d+)'
+    r')\s+(?P<title>\S.{0,84})$'
+)
+ANNEX_KW = ("ANNEX", "ANNEXE", "ANHANG", "ANLAGE")
+GDPR_HINT = re.compile(r'DSGVO|GDPR|2016/679|Verordnung \(EU\) 2016/679', re.I)
+
+
+def git_sha():
+    try:
+        return subprocess.check_output(["git", "-C", CP, "rev-parse", "--short", "HEAD"]).decode().strip()
+    except Exception:
+        return "unknown"
+
+
+CACHE_DIR = "/tmp/sge_cache"
+
+
+def _http_get(url, timeout=120.0, attempts=4):
+    import time as _t
+    last = None
+    for i in range(attempts):
+        try:
+            with httpx.Client(timeout=timeout, follow_redirects=True, headers={"User-Agent": "Mozilla/5.0"}) as c:
+                data = c.get(url).content
+            if data:
+                return data
+            last = RuntimeError("empty body")
+        except Exception as e:
+            last = e
+            log.info("download attempt %d/%d failed: %s", i + 1, attempts, e)
+        _t.sleep(3 * (i + 1))
+    raise RuntimeError("download failed after %d attempts: %s (%s)" % (attempts, url, last))
+
+
+def fetch(src):
+    import hashlib
+    os.makedirs(CACHE_DIR, exist_ok=True)
+    cache_key = hashlib.md5((src.get("zip_url") or src.get("pdf_url") or "").encode()).hexdigest()
+    cache_pdf = os.path.join(CACHE_DIR, cache_key + ".pdf")
+    if os.path.exists(cache_pdf):
+        pdf = open(cache_pdf, "rb").read()
+        if pdf[:4] == b"%PDF":
+            log.info("PDF cache hit %s (%d B)", cache_pdf, len(pdf))
+            return pdf
+    local = src.get("local", "")
+    if "zip_url" in src:
+        if local and os.path.exists(local):
+            data = open(local, "rb").read(); log.info("ZIP local %s (%d B)", local, len(data))
+        else:
+            data = _http_get(src["zip_url"], timeout=180.0)
+            log.info("ZIP downloaded (%d B)", len(data))
+        pdf = zipfile.ZipFile(io.BytesIO(data)).read(src["inner"])
+    else:
+        if local and os.path.exists(local):
+            pdf = open(local, "rb").read()
+        else:
+            pdf = _http_get(src["pdf_url"], timeout=120.0)
+    if pdf[:4] != b"%PDF":
+        raise RuntimeError("not a PDF: %r" % pdf[:16])
+    try:
+        open(cache_pdf, "wb").write(pdf)
+    except Exception:
+        pass
+    return pdf
+
+
+def src_url(src):
+    return src.get("zip_url") or src.get("pdf_url")
+
+
+def line_font(pg, ln):
+    chs = [c for c in pg.chars if ln["top"] - 1 <= c["top"] <= ln["bottom"] + 1]
+    if not chs:
+        return 0.0, ""
+    sz = Counter(round(c.get("size", 0), 1) for c in chs).most_common(1)[0][0]
+    fn = Counter(c.get("fontname", "") for c in chs).most_common(1)[0][0]
+    return sz, fn
+
+
+def _upper_ratio(title):
+    letters = [c for c in title if c.isalpha()]
+    return (sum(c.isupper() for c in letters) / len(letters)) if letters else 0.0
+
+
+_TTL_STOP = {"der", "die", "das", "den", "des", "dem", "und", "von", "zur", "zum", "für", "auf", "the",
+             "of", "and", "to", "for", "in", "on", "an", "under", "with", "sur", "aux"}
+
+
+def _ttoks(s):
+    out = set()
+    for t in re.findall(r'[a-zà-ÿ0-9]+', s.lower()):
+        t = re.sub(r'\d+$', '', t)  # Footnote-Ziffern entkleben ("freiwillig12" -> "freiwillig")
+        if len(t) > 2 and t not in _TTL_STOP:
+            out.add(t)
+    return out
+
+
+def _is_caption(title):
+    # strukturelle All-Caps-Überschrift (PREFACE/VORWORT/ANNEX) — auch ohne TOC-Eintrag gültig
+    return _upper_ratio(title) >= 0.8 and len(title) <= 34
+
+
+# TOC-Zeile: <enum> <Titel> <lange Punktführung> <Seitenzahl>. EIGENE Regex (NICHT ENUM —
+# dessen Titel-Cap .{0,84} scheitert an 130+-Punkt-Führungen). Titel = non-greedy bis zur Führung.
+TOC_LINE = re.compile(r'^[A-Z0-9][\w./()\-]*\s+(?P<title>.+?)\s*\.{3,}\s*\d{1,3}\s*$')
+
+
+def extract_toc(all_lines, body_size):
+    """TOC = explizite Strukturdeklaration des Dokuments. Sammelt die Titel-Token-Sets der
+    Inhaltsverzeichnis-Einträge aus dem Frontmatter. Absätze stehen NIE im TOC. >=5 => TOC vorhanden."""
+    titlesets = []
+    for page_number, txt, s, _ in all_lines:
+        if page_number > 8:
+            break
+        m = TOC_LINE.match(txt)
+        if not m:
+            continue
+        ts = _ttoks(m.group("title"))
+        if ts:
+            titlesets.append(ts)
+    return (len(titlesets) >= 5, titlesets)
+
+
+def _title_in_toc(title, titlesets):
+    # Match relativ zum BODY (inter/|body|): eine echte Überschrift ist ~ein TOC-Titel (alle Body-Wörter
+    # im TOC -> 1.0); ein Absatz, der Sektionswörter ENTHÄLT, hat Extra-Wörter (-> <0.75) -> kein Match.
+    # Body-Titel ist bei 84 Zeichen gekappt = Präfix des vollen TOC-Titels, daher robust gegen Kappung.
+    bt = _ttoks(title)
+    if not bt:
+        return False
+    for ts in titlesets:
+        if len(bt & ts) / len(bt) >= 0.75:
+            return True
+    return False
+
+
+def _looks_like_heading(txt, s, fn, body_size):
+    # F4-Plausibilitätsfilter — NICHT die Entscheidung (die trifft das TOC). Trimmt nur Fließtext weg,
+    # BEVOR das TOC befragt wird: kurz + nicht satzschließend. Bewusst KEIN Bold/Size-Zwang — das TOC
+    # entscheidet, Format ist nur Filter (greift eh nur in als unnummeriert erkannten Docs).
+    t = txt.rstrip()
+    return 3 <= len(t) <= 90 and not t.endswith((".", ";", ":", ","))
+
+
+def parse_guidance(pdf_bytes, noise):
+    with pdfplumber.open(io.BytesIO(pdf_bytes)) as pdf:
+        n_pages = len(pdf.pages)
+        szc = Counter()
+        all_lines = []  # (page_number, txt, size, fontname) — einmal extrahiert, zweimal genutzt
+        n_tables = 0       # detected (Capability: Tables Detect)
+        raw_tables = []    # (page_number, rows) — EXTRAHIERT (Capability: Tables Extract). Scope: gelinierte/einfache.
+        for pg in pdf.pages:
+            try:
+                pts = pg.extract_tables()
+            except Exception:
+                pts = []
+            n_tables += len(pts)
+            for tbl in pts:
+                # erster Scope: >=2 Zeilen + >=3 nicht-leere Zellen (keine verschachtelten/visuellen)
+                if tbl and len(tbl) >= 2 and sum(1 for row in tbl for c in row if c and c.strip()) >= 3:
+                    raw_tables.append((pg.page_number, [[(c or "").strip() for c in row] for row in tbl]))
+            for ch in pg.chars:
+                szc[round(ch.get("size", 0), 1)] += 1
+            for ln in pg.extract_text_lines(layout=False):
+                txt = " ".join(ln["text"].split())
+                if not txt:
+                    continue
+                s, fn = line_font(pg, ln)
+                all_lines.append((pg.page_number, txt, s, fn))
+        total_chars = sum(szc.values())
+        body_size = szc.most_common(1)[0][0]
+        # Schema-Erkennung (selbstkalibrierend): nutzt das Dok ALL-CAPS bare-Integer-Sektionsüberschriften
+        # (EDPB-Hausstil)? Dann nummeriert es auch Absätze (Satz-case) -> bare-Integer-Heading muss ALL-CAPS
+        # sein. WP243 (Title-case-Sektionen, keine ALL-CAPS) -> Regel inaktiv, keine Regression. Schwelle 2
+        # gegen einzelne Fluke-Caps-Zeile; EN/DE-formatierungsunabhängig (anders als bare-Integer-Magnitude).
+        caps_secs = 0
+        for _, txt, s, _ in all_lines:
+            if s < body_size - 0.5:
+                continue
+            mm = ENUM.match(txt)
+            if (mm and mm.group("ar") and "." not in mm.group("ar")
+                    and not re.search(r'\s\d{1,3}$', mm.group("title"))
+                    and _upper_ratio(mm.group("title")) >= 0.6):
+                caps_secs += 1
+        caps_scheme = caps_secs >= 2
+        toc_present, toc = extract_toc(all_lines, body_size)
+        # F4-DOC-Erkennung: decken NUMMERIERTE Headings das TOC ab? Wenn kaum -> die Headings sind unnummeriert
+        # (F4) -> unnummerierten Pfad aktivieren. Sonst (F1-F3) AUS -> keine Regression.
+        numbered_toc_hits = 0
+        if toc_present:
+            for _, txt, s, _ in all_lines:
+                if s < body_size - 0.5:
+                    continue
+                mm = ENUM.match(txt)
+                if not mm or re.search(r'\s\d{1,3}$', mm.group("title")):
+                    continue
+                ttl = mm.group("title").strip().rstrip('.').strip()
+                if _title_in_toc(ttl, toc) or _is_caption(ttl):
+                    numbered_toc_hits += 1
+        unnumbered_doc = toc_present and numbered_toc_hits < max(3, len(toc) * 0.4)
+        sections, cur, started, in_annex, seen_types, max_sec, un_count = [], None, False, False, set(), 0, 0
+        top_en, top_type = None, None  # last level-1 enumerator (roman/arabic/annex) for sub-path assembly
+        for page_number, txt, s, fn in all_lines:
+            m = ENUM.match(txt)
+            ok_head = bool(m) and s >= body_size - 0.5 and not re.search(r'\s\d{1,3}$', m.group("title"))
+            if ok_head:
+                if m.group("ar"): typ, en = "ar", m.group("ar")
+                elif m.group("ro"): typ, en = "ro", m.group("ro")
+                elif m.group("le"): typ, en = "le", m.group("le")
+                else: typ, en = "ax", m.group("ax")
+                title = m.group("title").strip().rstrip('.').strip()
+                # Großbuchstaben-Sub nur in römisch-Schema (Label-Assembly) — immer
+                if typ == "le" and top_type != "ro":
+                    ok_head = False
+                if ok_head and typ == "ar" and not in_annex:
+                    if unnumbered_doc:
+                        # F4-Doc: nummerierte Zeilen sind Absätze/Artikel-Refs ("13.1", "39.34"), nicht Headings
+                        # -> nur akzeptieren, wenn der Titel im TOC steht.
+                        if not _title_in_toc(title, toc):
+                            ok_head = False
+                    elif "." in en:
+                        # dotted = echte Sub-Headings (immer ok), AUSSER Dezimal/Uhrzeit-FP: führende Null im
+                        # Sub-Teil ("8.00 Uhr", "17.00"). Echte Sektionsnummern haben keine führende Null (3.1, 3.10).
+                        if any(c.startswith("0") for c in en.split(".")[1:]):
+                            ok_head = False
+                    elif toc_present:
+                        # bare-Integer: TOC = Wahrheit (Absätze stehen nie im TOC); All-Caps-Captions auch ohne TOC.
+                        if not (_title_in_toc(title, toc) or _is_caption(title)):
+                            ok_head = False
+                    else:  # Fallback-Heuristiken nur für bare-Integer (kein TOC)
+                        if top_type == "ro":
+                            ok_head = False
+                        elif caps_scheme and _upper_ratio(title) < 0.6:
+                            ok_head = False
+                        elif int(en) > max_sec + 5:
+                            ok_head = False
+            elif (m is None and unnumbered_doc and _looks_like_heading(txt, s, fn, body_size)
+                  and not re.search(r'\.\d+$', txt.strip()) and _title_in_toc(txt, toc)):
+                # F4 — UNNUMMERIERTE Überschrift: TOC = die ENTSCHEIDUNG, looks_like_heading nur Plausibilitätsfilter.
+                # Greift NUR wenn ENUM nicht matcht UND der Doc als unnummeriert erkannt wurde (F1-F3 unberührt).
+                # NICHT mit ".<Ziffern>" endend -> Fußnoten-Fragmente ("environment.47") raus (schont DE-Komposita).
+                ok_head, typ, en, title = True, "ut", None, txt.strip().rstrip('.').strip()
+            if ok_head:
+                seen_types.add(typ)
+                if typ == "le":
+                    num, lvl = ((top_en + "." + en) if top_en else en), 2
+                elif typ == "ut":
+                    un_count += 1
+                    num, lvl = str(un_count), 1
+                else:
+                    num = en
+                    lvl = (num.count('.') + 1) if typ == "ar" else 1
+                    top_en, top_type = en, typ
+                    if typ == "ar" and "." not in en and not in_annex:
+                        max_sec = max(max_sec, int(en))
+                cur = {"num": num, "title": title, "level": lvl, "in_annex": in_annex,
+                       "bold": "bold" in fn.lower(), "page": page_number, "body": []}
+                sections.append(cur); started = True
+                if typ == "ax" or any(k in (num + " " + title).upper() for k in ANNEX_KW):
+                    in_annex = True
+            elif started and cur is not None:
+                if s >= body_size - 2 and not noise.match(txt):
+                    cur["body"].append(txt)
+        # TOC-Duplikate generisch entfernen: gleicher LABEL-Key doppelt (TOC-Stub p2-3 + echte Sektion)
+        # -> den mit längerem Body behalten. Key = Label-Logik (A-Präfix für numerische Annex-Items),
+        # NICHT roher num — sonst kollidieren WP243-Annex-Items (num 1-13) mit Kapiteln (num 1-5).
+        def _lk(sc):
+            n = sc["num"]
+            return ("A" + n) if (sc.get("in_annex") and n.isdigit()) else n
+        best = {}
+        for sc in sections:
+            k = _lk(sc)
+            if k not in best or len("\n".join(sc["body"])) > len("\n".join(best[k]["body"])):
+                best[k] = sc
+        sections = [sc for sc in sections if best.get(_lk(sc)) is sc]
+        return {"pages": n_pages, "total_chars": total_chars, "body_size": body_size,
+                "sections": sections, "caps_scheme": caps_scheme, "schemes": seen_types,
+                "toc": toc_present, "toc_entries": len(toc),
+                "tables": n_tables, "annex": any(sc.get("in_annex") for sc in sections),
+                "raw_tables": raw_tables}
+
+
+def self_test(parsed, expected, artref, expected_refs):
+    probs, secs = [], parsed["sections"]
+    if parsed["total_chars"] < 5000:
+        probs.append("zu wenig embedded text (%d) -> OCR?" % parsed["total_chars"])
+    if len(secs) < expected:
+        probs.append("nur %d Sektionen < expected %d" % (len(secs), expected))
+    full = " ".join(t for sc in secs for t in sc["body"]) + " " + " ".join(sc["title"] for sc in secs)
+    arts = set(artref.findall(full))
+    # Manifest-Vertrag (doc_type-abhängig): Artikel-Pflicht NUR wenn der doc_type 'article' erwartet
+    # (Guidance/EU-VO). Technical Standards (NIST/ENISA) zitieren section/control -> kein FAIL bei fehlenden Artikeln.
+    if "article" in expected_refs and not arts:
+        probs.append("references_out: KEIN Artikel erkannt (doc_type erwartet article)")
+    return (not probs, probs, sorted(arts, key=lambda x: int(x))[:12])
+
+
+def _build_units_struct(doc_id, doc, lang, parsed, base_version, prov):
+    reg = doc["reg"]
+    units = []
+    sources = []
+    idx = 0
+    for sc in parsed["sections"]:
+        body = "\n".join(sc["body"]).strip()
+        if len(body) < 40:
+            continue
+        idx += 1
+        num = sc["num"]
+        lab = ("A" + num) if (sc.get("in_annex") and num.isdigit()) else num
+        cu = "%s §%s" % (reg, lab)
+        text = "%s §%s %s\n\n%s" % (reg, lab, sc["title"], body)
+        m = {
+            "regulation_code": reg, "regulation_short": reg, "regulation_name_de": doc["name"],
+            "language": lang, "citation_style": "guidance_section", "document_type": "guidance",
+            "source_class": "supervisory_guidance", "source_role": "interpretation", "use_for_primary": False,
+            "bindingness": "non_binding_interpretative", "authority_level": 70, "authority_weight": 70,
+            "source_type": "guidance", "issuer": doc["issuer"], "jurisdiction": "EU",
+            "source": "ec.europa.eu", "license": "public_eu", "category": "guidance",
+            "citation_unit": cu, "article_label": cu, "parent_citation_unit": reg, "is_citable": True,
+            "article": "§%s" % lab, "article_title": sc["title"], "article_type": "interpretation",
+            "chunk_scope": "guidance_section", "context_hierarchy": [reg],
+            "display_context": "%s > §%s %s" % (reg, lab, sc["title"]),
+            "norm_id": "EU-%s-%s-%s" % (doc_id, lang.upper(), lab),
+            "references_out": [], "child_tables": sc.get("child_tables", []),
+        }
+        m.update(prov)
+        units.append(UploadUnit(filename="%s_%s_s%d.txt" % (doc_id.lower(), lang, idx),
+                                text=text, meta=m,
+                                document_version="%s-s%d" % (base_version, idx),
+                                collection=BUILD_COLLECTION))
+        sources.append((body, sc["title"]))
+    return units, sources
+
+
+def _attach_refs(units, sources, artref):
+    for u, (body, title) in zip(units, sources):
+        u.meta["references_out"] = sorted({"Art. %s DSGVO" % n for n in artref.findall(body + " " + title)},
+                                          key=lambda x: int(x.split()[1]))
+
+
+def build_units(doc_id, doc, lang, parsed, base_version, prov, artref):
+    units, sources = _build_units_struct(doc_id, doc, lang, parsed, base_version, prov)
+    _attach_refs(units, sources, artref)
+    return units
+
+
+def _table_md(rows):
+    hdr = rows[0]
+    out = ["| " + " | ".join(c or "" for c in hdr) + " |",
+           "| " + " | ".join("---" for _ in hdr) + " |"]
+    for r in rows[1:]:
+        out.append("| " + " | ".join((c or "").replace("\n", " ") for c in r) + " |")
+    return "\n".join(out)
+
+
+def build_table_units(doc_id, doc, lang, parsed, base_version, prov):
+    # Capability Tables Extraction: jede Tabelle = EIGENE Knowledge-Unit (Markdown + JSON), an ihre Sektion gehängt.
+    # table.parent_section = section.num ; section.child_tables = [table_id]. Separater Pfad — Heading-Parse unberührt.
+    reg, sections, units = doc["reg"], parsed["sections"], []
+    for ti, (page, rows) in enumerate(parsed.get("raw_tables", []), 1):
+        parent = None
+        for sc in sections:
+            if sc.get("page", 0) <= page:
+                parent = sc
+            else:
+                break
+        plabel = parent["num"] if parent else "0"
+        md = _table_md(rows)
+        if len(md) < 25:
+            continue
+        tid = "%s-t%d" % (doc_id, ti)
+        if parent is not None:
+            parent.setdefault("child_tables", []).append(tid)
+        cu = "%s §%s Tabelle %d" % (reg, plabel, ti)
+        m = {"regulation_code": reg, "regulation_short": reg, "language": lang,
+             "source_class": "supervisory_guidance", "source_role": "interpretation", "use_for_primary": False,
+             "jurisdiction": "EU", "category": "guidance",
+             "is_table": True, "table_id": tid, "parent_section": plabel, "page": page,
+             "columns": rows[0], "rows": rows, "markdown": md,
+             "extraction_method": "pdfplumber", "confidence": "lined-simple",
+             "citation_unit": cu, "article_label": cu, "chunk_scope": "table",
+             "display_context": "%s > §%s > Tabelle %d" % (reg, plabel, ti), "references_out": []}
+        m.update(prov)
+        units.append(UploadUnit(filename="%s_%s_tbl%d.txt" % (doc_id.lower(), lang, ti),
+                                text="%s §%s — Tabelle %d (S.%d)\n\n%s" % (reg, plabel, ti, page, md),
+                                meta=m, document_version="%s-tbl%d" % (base_version, ti),
+                                collection=BUILD_COLLECTION))
+    return units
+
+
+import capability_pipeline as _CP
+
+
+def _region_native_tables(pdf_bytes):
+    rt = []
+    regions = []
+    with pdfplumber.open(io.BytesIO(pdf_bytes)) as _pdf:
+        for pg in _pdf.pages:
+            try:
+                tables = pg.find_tables()
+            except Exception:
+                tables = []
+            for t in tables:
+                try:
+                    tbl = t.extract()
+                except Exception:
+                    tbl = None
+                if tbl and len(tbl) >= 2 and sum(1 for row in tbl for c in row if c and c.strip()) >= 3:
+                    rt.append((pg.page_number, [[(c or "").strip() for c in row] for row in tbl]))
+                    regions.append((pg.page_number, t.bbox))
+    return rt, regions
+
+
+class _C6Tables:
+    name = "C6_Tables"
+    consumes = ["table_region"]
+    produces = ["table_units"]
+    def run(self, ctx):
+        rtables, regions = _region_native_tables(ctx["pdf"])
+        ctx["claimed_table_regions"] = regions
+        p2 = dict(ctx["parsed"])
+        p2["raw_tables"] = rtables
+        ctx["_table_units"] = build_table_units(ctx["doc_id"], ctx["doc"], ctx["lang"], p2, ctx["rt"], ctx["prov"])
+
+
+class _C7ReadingOrder:
+    name = "C7_ReadingOrder"
+    consumes = ["prose_region", "table_units"]
+    produces = ["ordered_prose"]
+    def run(self, ctx):
+        ctx["_c7_owns_prose"] = True
+
+
+class _C1C2Sections:
+    name = "C1C2_Sections"
+    consumes = ["prose_region", "table_units"]
+    produces = ["section_struct"]
+    def run(self, ctx):
+        units, sources = _build_units_struct(ctx["doc_id"], ctx["doc"], ctx["lang"], ctx["parsed"], ctx["rt"], ctx["prov"])
+        ctx["_section_units"] = units
+        ctx["_sources"] = sources
+
+
+class _C4References:
+    name = "C4_References"
+    consumes = ["section_struct"]
+    produces = ["references"]
+    def run(self, ctx):
+        _attach_refs(ctx["_section_units"], ctx["_sources"], ctx["cfg"]["artref"])
+
+
+def run_engine(doc_id, doc, lang, parsed, run_tag, prov, cfg, pdf_bytes):
+    ctx = {"doc_id": doc_id, "doc": doc, "lang": lang, "parsed": parsed,
+           "rt": run_tag, "prov": prov, "cfg": cfg, "pdf": pdf_bytes}
+    caps = [_C4References(), _C7ReadingOrder(), _C1C2Sections(), _C6Tables()]
+    order = _CP.resolve_order(caps, {"table_region", "prose_region"})
+    for c in order:
+        c.run(ctx)
+    return ctx["_section_units"], ctx["_table_units"]
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--doc", required=True)
+    ap.add_argument("--lang", required=True, choices=["en", "de"])
+    ap.add_argument("--dry-run", action="store_true")
+    args = ap.parse_args()
+    doc = DOCS[args.doc]
+    lang, cfg = args.lang, LANG[args.lang]
+    sha = git_sha()
+    run_tag = "2026.1-%s-%s" % (args.doc.lower(), lang)
+    run_id = "%s-%s" % (run_tag, int(time.time()))
+    date = datetime.date.today().isoformat()
+    src = doc["sources"][lang]
+    log.info("SGE %s [%s] | exp_sec=%d sha=%s dry=%s", args.doc, lang, doc["expected_sections"], sha, args.dry_run)
+
+    doc_type = doc.get("doc_type") or ISSUER_DOCTYPE.get(doc["issuer"], "Guidance")
+    expected_refs = DOC_TYPE_REFS.get(doc_type, ["article"])
+    pdf = fetch(src)
+    _t0 = time.time()
+    parsed = parse_guidance(pdf, cfg["noise"])
+    parse_ms = int((time.time() - _t0) * 1000)
+    ok, probs, arts = self_test(parsed, doc["expected_sections"], cfg["artref"], expected_refs)
+    pages = parsed["pages"]
+    log.info("PARSED pages=%d chars=%d body=%.1f sections=%d caps_scheme=%s gate=%s %s",
+             pages, parsed["total_chars"], parsed["body_size"], len(parsed["sections"]), parsed.get("caps_scheme"), ok, probs)
+    log.info("Art-refs: %s", arts)
+    for sc in parsed["sections"]:
+        log.info("  [%s]%s %s (p%d)", sc["num"], "*" if sc.get("in_annex") else " ", sc["title"][:58], sc["page"])
+
+    sch = parsed.get("schemes", set())
+    family = ("unnumbered-toc" if "ut" in sch and not ({"ar", "ro"} & sch)  # F4
+              else "roman-hierarchical" if "ro" in sch                       # F2
+              else "arabic-caps+paragraph" if parsed.get("caps_scheme")      # F3
+              else "arabic-hierarchical")                                    # F1
+    detF = FAMILY_F.get(family, "?")
+    expF = EXPECTED_FAMILY.get(args.doc, "?")
+    gate = "OK" if expF == detF else ("REVIEW(exp=%s)" % expF if expF != "?" else "no-expected")
+    prov = {"parser_version": "StructuredGuidanceExtractor@%s+sge_build" % sha, "ingest_run_id": run_id,
+            "ingest_date": date, "source_url": src_url(src), "source_inner_file": src.get("inner", ""),
+            "build_collection": BUILD_COLLECTION, "manifest_version": MANIFEST_VERSION, "document_id": args.doc,
+            "layout_family": detF, "document_type": doc_type, "expected_reference_types": expected_refs}
+    units, table_units = run_engine(args.doc, doc, lang, parsed, run_tag, prov, cfg, pdf)  # CUTOVER: Engine-Pfad
+    total_refs = sum(len(u.meta["references_out"]) for u in units)
+    detect = "TOC(%d)" % parsed.get("toc_entries", 0) if parsed.get("toc") else "heuristic"
+    log.info("UNITS=%d table_units=%d | VITALS family=%s(%s) FAMILY-GATE=%s detect=%s tables_detect=%d tables_extract=%d annex=%s parse_ms=%d chunks/page(units)=%.2f refs/unit=%.2f",
+             len(units), len(table_units), family, detF, gate, detect, parsed.get("tables", 0), len(table_units),
+             "Y" if parsed.get("annex") else "N", parse_ms, len(units) / max(pages, 1), total_refs / max(len(units), 1))
+    if gate.startswith("REVIEW"):
+        log.warning("FAMILY-GATE REVIEW: %s erwartet %s, erkannt %s — Klassifikation prüfen", args.doc, expF, detF)
+    if units:
+        m = units[0].meta
+        pk = ["parser_version", "ingest_run_id", "ingest_date", "source_url", "build_collection", "manifest_version", "document_id"]
+        log.info("sample: label=%r language=%r source_class=%r use_for_primary=%r refs=%s",
+                 m.get("article_label"), m.get("language"), m.get("source_class"), m.get("use_for_primary"), m.get("references_out"))
+        log.info("provenance present: %s", all(k in m for k in pk))
+
+    if args.dry_run:
+        import json as _json, hashlib as _hl
+        _g=[{"id":u.document_version,"filename":u.filename,"kind":("table" if u.meta.get("is_table") else "section"),
+             "text_sha":_hl.sha256((u.text or "").encode()).hexdigest()[:16],"meta":u.meta} for u in (units+table_units)]
+        _bp="/tmp/baseline_%s_%s.json"%(args.doc,lang)
+        open(_bp,"w").write(_json.dumps(_g,ensure_ascii=False,indent=1,default=str))
+        log.info("DRY RUN baseline -> %s (%d units: %d section + %d table)",_bp,len(_g),len(units),len(table_units)); return
+    if not ok:
+        log.error("GATE FAILED — aborting"); sys.exit(1)
+    n = 0
+    with httpx.Client(timeout=600.0, verify=False) as c:
+        for u in units + table_units:
+            n += upload_unit(c, RAG_URL, u)
+    log.info("UPLOADED: %d section + %d table units -> %d chunks", len(units), len(table_units), n)
+
+
+if __name__ == "__main__":
+    main()