audit pipeline v1.2: canonical business record as diff baseline (owner-verified truth), website JSON-LD hours, single-command e2e with gate
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@@ -144,21 +144,29 @@ def classify(fj, raw):
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else:
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mat.append((sev, t, f"google='{ev.get('google')}' vs apple='{ev.get('apple')}'"))
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elif t.startswith("Hours mismatch"):
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days = ev or []
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diffs, parses = [], []
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for d in days:
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r = day_eq(d.get("google"), d.get("apple_maps"))
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if r == "diff": diffs.append(d)
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elif r == "parse": parses.append(d)
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if diffs:
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mat.append((sev, t, f"{len(diffs)} day(s) genuinely differ: " +
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"; ".join(f"{d['day']}: {d['google']} vs {d['apple_maps']}" for d in diffs[:4])))
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elif parses:
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nv.append((sev, t, f"{len(parses)} day(s) affected by '1800' parse artifact: " +
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"; ".join(f"{d['day']}: {d['google']} vs {d['apple_maps']}" for d in parses[:3]) +
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" — verify against live Apple Maps; excluded from report until confirmed"))
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if isinstance(ev, dict) and "deviations" in ev:
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# reference mode (canonical or GBP): engine parse-normalized, deviations are real
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devs = ev.get("deviations") or []
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lbl = dict(SURFACES)
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mat.append((sev, t, f"{len(devs)} day(s) deviate from reference: " +
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"; ".join(f"{d['day']}: " + ", ".join(f"{lbl[k]} “{v}”" for k, v in d.items() if k in lbl)
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for d in devs[:4])))
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else:
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fp.append((sev, t, "format only: '8 AM' vs '8:00 AM' — identical after parse"))
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days = ev or []
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diffs, parses = [], []
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for d in days:
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r = day_eq(d.get("google"), d.get("apple_maps"))
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if r == "diff": diffs.append(d)
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elif r == "parse": parses.append(d)
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if diffs:
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mat.append((sev, t, f"{len(diffs)} day(s) genuinely differ: " +
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"; ".join(f"{d['day']}: {d['google']} vs {d['apple_maps']}" for d in diffs[:4])))
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elif parses:
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nv.append((sev, t, f"{len(parses)} day(s) affected by '1800' parse artifact: " +
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"; ".join(f"{d['day']}: {d['google']} vs {d['apple_maps']}" for d in parses[:3]) +
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" — verify against live Apple Maps; excluded from report until confirmed"))
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else:
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fp.append((sev, t, "format only: '8 AM' vs '8:00 AM' — identical after parse"))
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elif t.startswith("Website URL inconsistent"):
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gu, au = ev.get("google"), ev.get("apple")
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if url_norm(gu) == url_norm(au):
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@@ -189,8 +197,8 @@ def classify(fj, raw):
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mat.append((sev, t, f"no structured data at {ev.get('url')}"))
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elif t.startswith("Review count varies"):
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fp.append((sev, t, "low-signal: platforms count reviews differently; not an integrity issue"))
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elif "review recency" in t.lower():
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fp.append((sev, t, "tooling gap: scraper captures no review dates (documented limitation)"))
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elif "review recency" in t.lower() or t.lower().startswith("most recent review"):
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fp.append((sev, t, "low-signal: review velocity not measurable from ≤3-review samples (tooling ceiling); activity judged from GBP rating/count"))
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elif t.startswith("Category varies"):
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fp.append((sev, t, "low-signal: per-platform taxonomy (Apple 'consumer sector' is its default)"))
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elif t.startswith("Price level") or t.startswith("Only 0%") or "Open Graph" in t:
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@@ -231,7 +239,13 @@ def finding_text(kind, evidence, why=""):
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elif kind == "address":
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obs = f"Google lists “{evidence.get('google')}”. Apple Maps lists “{evidence.get('apple')}”."
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elif kind == "hours":
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if isinstance(evidence, list):
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if isinstance(evidence, dict) and "deviations" in evidence:
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lbl = dict(SURFACES)
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ref_txt = ", ".join(f"{d.capitalize()} “{v}”" for d, v in sorted((evidence.get("reference_hours") or {}).items()))
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obs = (f"Reference ({evidence.get('reference')}): {ref_txt}. Deviations: " +
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"; ".join(f"{d['day'].capitalize()} — " + ", ".join(f"{lbl[k]} “{v}”" for k, v in d.items() if k in lbl)
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for d in (evidence.get("deviations") or [])))
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elif isinstance(evidence, list):
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obs = "; ".join(f"{d['day']}: Google “{d['google']}” vs Apple “{d['apple_maps']}”" for d in evidence)
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else:
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obs = evidence if isinstance(evidence, str) else json.dumps(evidence)[:200]
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@@ -329,13 +343,13 @@ def build_report(run_dir, name, mat, nv, raw):
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k = kinds[i]
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raw_ev = next((f.get("evidence") for f in fj["findings"] if f["title"] == title), evtext)
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obs = raw_ev if (isinstance(raw_ev, str) or (isinstance(raw_ev, (dict, list)) and raw_ev)) else evtext
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L += [f"### Finding {n}: {title.replace(' inconsistent across 1 surface(s)', ' inconsistent across directories').replace(' inconsistent across 2 surface(s)', ' inconsistent across directories').replace(' on 5 day(s) — GBP is authoritative', ' between Google and Apple').replace(' on 7 day(s) — GBP is authoritative', ' between Google and Apple').replace(' on 4 day(s) — GBP is authoritative', ' between Google and Apple')}", ""]
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L += [f"### Finding {n}: {title.replace(' — owner-verified hours', ' against owner-verified schedule').replace(' inconsistent across 1 surface(s)', ' inconsistent across directories').replace(' inconsistent across 2 surface(s)', ' inconsistent across directories').replace(' on 5 day(s) — GBP is authoritative', ' between Google and Apple').replace(' on 7 day(s) — GBP is authoritative', ' between Google and Apple').replace(' on 4 day(s) — GBP is authoritative', ' between Google and Apple')}", ""]
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L += [f"**Observation:** ", finding_text(k, obs, evtext) if not isinstance(obs, str) else obs, ""]
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L += ["**Impact:** ", IMPACT.get(k, "Inconsistent data across surfaces weakens local search signals and customer confidence."), ""]
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rec = {
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"phone": "Confirm the correct primary number, then update every directory to match. Keep the verified number identical across Google, Apple, and the website.",
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"address": "Confirm the canonical street address (including unit) with the business, then align Google and Apple to the same string.",
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"hours": "Verify actual operating hours with the business. Update the non-authoritative surface to match Google's verified schedule, or correct Google if its hours are inaccurate.",
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"hours": "Verify actual operating hours with the business, then update every surface whose hours deviate from the verified schedule above (directories and website schema).",
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"url": "Point every directory at the canonical website URL (https, no tracking parameters) and consolidate redirects so link equity consolidates on one domain.",
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"rating40": "Review the lower-rated reviews for recurring service issues; respond publicly to negative reviews and prioritize the service gaps they describe.",
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"ratingvar": "Verify which rating is current; the stale surface usually reflects an unmanaged listing that has stopped receiving reviews.",
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