alivirgo Verify Names, Locations, & Entities (Entity Grounding) (AI Skill)
Overview
Large Language Models do not possess a live GPS or relational database of human beings; they generate names and locations based on statistical token probability.
When asked to generate market research ("Who is the Head of Procurement at Company X?" or "List 5 boutique coffee roasters in Austin, Texas"), AI models frequently synthesize plausible-sounding names, fake street addresses, and nonexistent executives.
The Entity Grounding Protocol establishes strict rules for detecting entity hallucinations and verifying proper nouns against external ground truth.
Statistical Plausibility vs. Ground-Truth Reality
┌─────────────────────────────────────────────────────────────┐
│ Entity Hallucination Trap │
│ │
│ User asks: "Who is the Chief AI Officer at Acme Corp?" │
│ │ │
│ ▼ │
│ [ LLM TOKENS GENERATE STATISTICAL PLAUSIBILITY ]: │
│ "Dr. Sarah Jenkins, former MIT AI Lab researcher..." │
│ ↳ Plausible sounding, perfectly formatted, 100% FICTION │
│ │ │
│ ▼ │
│ [ THE 4-POINT GROUNDING LADDER (External Verification) ]: │
│ 1. Check LinkedIn / Company 'About' Page │
│ 2. Verify Secretary of State Corporate Filings │
│ 3. Verify Street Address on Google Maps / Postal DB │
│ 4. Search Official Press Releases for Appointment Date │
└─────────────────────────────────────────────────────────────┘
The 4 High-Risk Entity Danger Zones
- Executive & Staff Names: Corporate turnover is rapid; models hallucinate past or fictional personnel.
- Physical Street Addresses & Suite Numbers: Models generate realistic-looking street numbers that lead to empty lots.
- Local Business Names & Phone Numbers: Phone numbers and store hours change frequently.
- Legal & Court Case Citations: Precedent case names (e.g. Smith v. Johnson Corp) are notoriously confabulated.
Master Entity Verification Prompt Templates
Pattern 1: The Named Entity Sourcing Directive
Use when conducting market intelligence, B2B lead generation, or competitive research:
List the key leadership team at [COMPANY].
Strict Entity Grounding Rules:
1. For every individual listed, provide their **exact job title** and the source URL where this is verified.
2. If their current role cannot be confirmed via your live search tool, mark as: `[STATUS UNVERIFIED / REQUIRES MANUAL CHECK]`.
3. Do NOT extrapolate or guess executive names based on past news.
Pattern 2: The Physical Location & Address Guardrail
Use for event planning, logistics, or real estate analysis:
Suggest 3 conference venues in [CITY] that can host 200 attendees.
- State the exact venue name, neighborhood, and physical street address.
- Flag any venue that you are unsure is currently in business.
Real-World Case Study
Scenario: Preparing a B2B Sales Outreach List
The AI Hallucination Failure
An SDR prompted AI: "List the VP of Security at 5 mid-market fintech companies."
- The AI generated 5 names.
- The SDR sent personalized cold emails.
- 3 of the 5 people never worked at those companies; 1 had left 3 years prior.
- Result: High bounce rate and damaged sender domain reputation.
The Entity Grounded Workflow
The SDR added the Grounding Rule: "List the companies and their current publicly stated Security hiring initiatives; provide LinkedIn search query links rather than generating specific personal names."
- The SDR clicked the 5 verified LinkedIn search links in 60 seconds, identified the exact active VPs, and sent emails with 100% accuracy.
Summary Best Practices
- Never send an email to an AI-generated person's name without checking LinkedIn: Takes 10 seconds and prevents major embarrassment.
- Search address coordinates: Always paste addresses into Google Maps before booking travel or printing collateral.