Entity Audit

Entity Audit — Phase 9

Executive Summary

Entity optimization is the bridge between traditional SEO and AI search visibility. Google's Knowledge Graph powers both the traditional Knowledge Panel and AI Overviews — businesses with strong entity signals get cited in AIO at higher rates than those without. In 2025, the minimum viable entity footprint is: GBP verified + 7+ sameAs connections + consistent NAP across all platforms + Wikidata entity. ChatGPT and Perplexity reference businesses with complete Wikidata entries 3× more than businesses without. The 30-minute fix with the highest impact: add sameAs properties to LocalBusiness schema (links to GBP, Facebook, LinkedIn, Yelp, BBB, Instagram, Wikidata) — this single action strengthens entity consolidation significantly.

2025 entity benchmarks:

• sameAs count for Knowledge Panel eligibility: 7+ authoritative sources (Google's Knowledge Vault threshold)
• sameAs 0–3: weak entity — Knowledge Panel very unlikely; 7–9: competitive; 10+: strong
• E-E-A-T entity signal: named author/owner with Person schema = measurable trust increase for YMYL and local service categories
• AI recognition rate: businesses with Wikidata entry cited 3× more by ChatGPT/Perplexity
• knowsAbout schema property: signals topical expertise domain directly to Google NLP
• Most specific @type matters: PlumbingContractor not LocalBusiness — specificity = entity clarity

Numbered Action Plan:

Immediate (30-Minute Wins)

1. Add sameAs links to LocalBusiness schema — Add "sameAs": [...] array with GBP URL, Facebook, LinkedIn, Yelp, BBB, Instagram, Twitter/X, Wikidata (if exists). Each is a separate entity consolidation signal. Effort: 30 min. Priority: 25 (5×5).
2. Change @type to most specific type — If currently "@type": "LocalBusiness", change to specific type (e.g., PlumbingContractor, Dentist, Attorney). Effort: 5 min. Priority: 15.
3. Add knowsAbout to Organization schema — Add the 5–7 core service entities the business is authoritative on. This directly signals topical expertise to Google's NLP and AI systems. Example: "knowsAbout": ["Drain Cleaning", "Water Heater Installation", "Emergency Plumbing", "Sewer Line Repair", "Tankless Water Heaters"]. Also add knowsAbout to Person schema for the owner/key staff. Effort: 15 min. Priority: 20.

Short-Term (Week 1–2)

4. Create Wikidata entity — If business has significant web presence (news mentions, 3+ external references): create Wikidata entry with name, type, location, website, founding date, sameAs links. Effort: 2–4 hrs. Priority: 16.
5. Add Person schema for owner — Create author page for business owner with Person schema: name, jobTitle (specific), credentials, worksFor → business entity, sameAs → LinkedIn. Effort: 1–2 hrs. Priority: 16.
6. Fix entity attribute inconsistencies — Audit: business name format, address format, phone format across website/GBP/Yelp/BBB/Facebook. Any variation = entity fragmentation. Fix the ≤3 platforms causing inconsistency. Effort: 1–2 hrs. Priority: 20.
7. Validate all schema — Run Rich Results Test (search.google.com/test/rich-results) + Schema Markup Validator (validator.schema.org) on homepage and top service pages. Fix any errors (invalid @type, missing required properties). Effort: 1–2 hrs. Priority: 20.

Medium-Term (Month 1–2)

8. Build topical entity associations — Publish 25+ articles on primary service cluster (entity co-occurrence accumulates). Get cited alongside industry terms in local publications. Add about + mentions schema to key pages. Effort: Ongoing.
9. Create team author pages — For each key team member: create /team/[name]/ page with Person schema, headshot, credentials, linked blog posts. Strengthens E-E-A-T author entity signals. Effort: 2–4 hrs/person.
10. Earn entity mentions — Pitch local news, industry publications, community platforms. Each mention = entity co-citation = strengthened Knowledge Graph. Effort: 4–8 hrs/mention.

Why Entities Matter in 2025–2026

Google's search engine is fundamentally entity-based. Rankings are increasingly driven by whether Google understands:

1. What your business IS — entity type, attributes, relationships
2. What topics you're authoritative on — topical entity associations
3. Where you exist — local entity with verified location signals
4. Who you're connected to — entity relationships: people, orgs, places

Strong entity presence = trust = better rankings AND AI visibility. In 2025:

• AI Overviews pull entity-structured data as primary citation sources
• ChatGPT/Perplexity reference businesses with complete Wikidata entries 3× more
• Google's Knowledge Vault — entities with sameAs coverage from 7+ authoritative sources achieve Knowledge Panel significantly faster

Step 1: Read Project Context

Read {AUDIT_DIR}/intake-data.md — business name, URL, location, services. Read {AUDIT_DIR}/competitor-profiles.md — competitor entity signals. Read {AUDIT_DIR}/local-findings.md — GBP completeness (feeds entity graph).

Tools for this phase:

2025 Entity Context: Google's "Entity Home" concept means each business should have one authoritative page consolidating all entity attributes. An incomplete entity = fragmented Knowledge Graph = weaker rankings. Wikidata entry + GBP + 7+ sameAs connections = minimum viable entity footprint for Knowledge Panel eligibility.

Step 2: Business Entity Recognition Tests

Test 1: Knowledge Panel Check

Search Google for each:

Status categories:

• ✅ Full Knowledge Panel — entity well recognized
• ⚠️ Partial — GBP card shows but no full panel (entity weak)
• ❌ No panel — entity not in Knowledge Graph

Test 2: AI Assistant Entity Recognition

Test 3: Wikidata Check

Visit wikidata.org → search business name:

• Wikidata entity exists? Q-number: [Q_______]
• If exists: attribute completeness (names, location, website, founded, industry, sameAs links)?
• If not: does business qualify? (Must have coverage in reliable external sources, not just website)

Step 3: Entity Attribute Consistency

For a local business entity, Google expects these attributes consistently across ALL web properties:

Inconsistencies = entity confusion = fragmented Knowledge Graph = weaker rankings and AI citations.

Specific @type Recommendations

Use the most specific Schema.org @type available — not just "LocalBusiness":

Step 4: sameAs Entity Connections

sameAs creates a web of entity signals Google uses to consolidate and strengthen the business entity.

sameAs Inventory Audit (from website's LocalBusiness schema)

sameAs count benchmarks:

• 0–3 sameAs: ❌ Weak entity — Knowledge Panel very unlikely
• 4–6 sameAs: ⚠️ Developing — partial entity recognition possible
• 7–9 sameAs: ✅ Competitive — Knowledge Panel achievable
• 10+ sameAs: ✅✅ Strong — full entity consolidation likely

Current sameAs count: [X] / 11 recommended

Step 5: Content Entity Analysis (NLP)

Entity Extraction from Key Pages

For each key page (homepage + top 3 service pages), paste content into Google Cloud NLP API or InLinks:

# Google Cloud NLP API (free usage)
# Go to: console.cloud.google.com/natural-language
# Analyze Entities → paste page content → view salience scores

For each page, document:

• Key entities found: [entity name | type | salience score 0–1]
• Missing entities: what should be on the page based on topic but isn't?
• Entity sentiment: are key service terms associated with positive sentiment?

Entity Coverage vs. Top Competitors

For each service page, compare entity presence:

Step 6: E-E-A-T Entity Signals

Google's E-E-A-T assessment is entity-driven — these are measurable signals per page/domain:

Experience & Expertise Signals

Authority Signals

Trustworthiness

Step 7: People Entities (Owner + Team)

For service businesses, key people should be recognized entities — increases E-E-A-T and author trust:

Person entity schema (add to author pages):

{
  "@context": "https://schema.org",
  "@type": "Person",
  "name": "[Full Name]",
  "jobTitle": "[Title — be specific: 'Master Plumber' not 'Staff']",
  "description": "[Brief bio with credentials, years of experience, specialty]",
  "knowsAbout": ["Drain Cleaning", "Water Heater Installation", "Emergency Plumbing"],
  "worksFor": {
    "@type": "LocalBusiness",
    "@id": "https://domain.com/#business"
  },
  "sameAs": [
    "https://www.linkedin.com/in/[handle]/",
    "https://domain.com/team/[name]/"
  ]
}

Step 7b: Speakable Schema for AI Assistants

The speakable property (Schema.org) marks content sections that are especially suitable for text-to-speech and AI assistant extraction. Google supports speakable for news articles, and AI assistants (Siri, Gemini, Alexa) use it as a signal for which content to read aloud or extract as voice answers.

When to Use Speakable

• Service pages with clear answer paragraphs
• FAQ pages with concise Q&A pairs
• About page with business description
• Any page with a "definition block" answering "What is [service]?"

Speakable Implementation

Add to Article, WebPage, or FAQPage schema:

{
  "@context": "https://schema.org",
  "@type": "WebPage",
  "name": "[Page Title]",
  "speakable": {
    "@type": "SpeakableSpecification",
    "cssSelector": [".service-intro", ".faq-answer", "#business-description"]
  }
}

Rules for speakable content:

• Keep speakable sections to 2–3 short sentences each
• Use clear, conversational language (avoid jargon without definitions)
• Ensure the section makes sense when read aloud without visual context
• Point CSS selectors at the most citable, answer-rich paragraphs
• Do NOT mark the entire page as speakable — only the best answer blocks

Speakable Audit Checklist

Effort: 15 min/page. Impact: Improved voice search + AI assistant citation likelihood.

Step 8: Topical Entity Associations

Which topics does Google associate this business with? Test:

• Search [Business Name] [service term] → does client appear for this association?
• Search [service term] [city] → does Knowledge Panel reference client?
• Ask ChatGPT: "What is [Business Name] known for?" → does answer match target services?

Building topical associations:

1. Publish minimum 25 articles on the target topic cluster (entity co-occurrence accumulates)
2. Add knowsAbout to Organization schema with core topic entities
3. Get mentioned alongside industry terms in authoritative publications (entity co-citation)
4. Use about and mentions schema properties on key pages

Priority Matrix

Scoring

Output

Write to {AUDIT_DIR}/entity-findings.md with YAML frontmatter:

---
skill: local/entity-audit
phase: 9
date: [YYYY-MM-DD]
business: [Business Name]
url: [URL]
score: [X/100]
knowledge_panel: [present|partial|missing]
same_as_count: [X]
wikidata_entity: [yes|no|Q-number]
---

Include:

• Score X/100 with per-category breakdown
• Entity recognition status (Knowledge Panel + AI assistant tests)
• sameAs connection inventory (present vs. missing, priority-ranked)
• Attribute consistency gaps table (all platforms)
• NLP entity analysis (salience gaps vs. top competitors)
• E-E-A-T entity signals checklist (all 3 dimensions)
• People entity status (owner, team schema)
• Topical entity association strength per target topic
• Priority matrix (all actions, Impact × Feasibility scored)
• 30/90/180-day entity optimization roadmap

Output files:

• {AUDIT_DIR}/entity-findings.md — entity audit with score and schema gap list
• {REPORTS_DIR}/phase-9-entity-audit.pdf — auto-generated PDF after phase completes

Key consumers:

• local/brand-serp — Knowledge Panel and sameAs directly feed brand SERP
• local/local-seo — entity signals strengthen GBP and local pack
• output/report-generation — entity score in master report section 9

Entity Optimization Quick Reference

sameAs Source Priority Table (AI Visibility Ranked)

Brand mentions correlate 3× more strongly with AI visibility than backlinks (Ahrefs Dec 2025, 75K brands). The sameAs priority order below is ranked by AI citation impact, not just traditional SEO value:

GBP 2025 Entity Signals

GBP is the #1 entity signal for local businesses (Whitespark 2024). For maximum entity recognition:

• Primary category must match your core service (plumber → "Plumber", not "Contractor")
• Add all relevant secondary categories (5+ where applicable)
• Enable Q&A — unanswered questions weaken entity completeness
• Add service menu with prices — Google uses service data for Knowledge Panel display
• Enable Google Reserve/Booking if applicable — feeds into entity completeness score

INP + Entity Note

Entity strength affects Knowledge Panel display which appears for branded searches. Better entity recognition → stronger branded SERP → higher branded CTR. INP <200ms is required for all pages with entity schema to be fully crawled and processed.