DE-3: Data Cleaning at Scale

Clean this person's name: {{raw_name}}

Rules:
- Proper case for first and last name (capitalize first letter only)
- Remove titles, suffixes, and parenthetical notes from the name field
- Remove extra spaces
- Return format: {"first_name": "...", "last_name": "..."}

If the name appears to be a company name or email address, return {"first_name": "REVIEW", "last_name": "REVIEW"}

Standardize this job title: {{raw_title}}

Rules:
- Proper case
- Expand common abbreviations: VP → Vice President, Dir → Director, Mgr → Manager
- Remove symbols and special characters
- Keep the core title, remove department suffixes if overly long

Return ONLY the cleaned title.

Normalize this company name: {{raw_company}}

Rules:
- Remove legal suffixes if redundant (Inc, Corp, Ltd at the end) UNLESS the name is very short and removing it would cause confusion
- Proper case
- Remove trailing/leading spaces
- Expand obvious abbreviations only if unambiguous

Return ONLY the normalized company name.

Validate the format of this email address: {{raw_email}}

Check for:
- Valid format (has @, valid TLD, no spaces)
- Common typos (double dots, missing TLD, space in address)

Return: {"valid": true/false, "cleaned_email": "corrected version or original", "issue": "description of problem or null"}

Given this contact record:
Name: {{first_name}} {{last_name}}
Email: {{email}}
Company: {{company_name}}
Title: {{job_title}}

And this existing record:
Name: {{existing_first}} {{existing_last}}
Email: {{existing_email}}
Company: {{existing_company}}
Title: {{existing_title}}

Are these the same person? Consider: same name + same company = very likely duplicate; same email = definitive duplicate.

Return: {"is_duplicate": true/false, "confidence": "high/medium/low", "reason": "brief explanation"}

Based on this contact's LinkedIn URL {{linkedin_url}}, estimate data freshness:
(Use web research if available, otherwise estimate based on the following)

Current data in our record:
- Title: {{job_title}}
- Company: {{company_name}}

Questions to assess:
1. Is this company still active?
2. Is this title realistic for this company's size?
3. Any signals this contact has left (e.g., if you can check LinkedIn)?

Return: {"likely_current": true/false, "confidence": "high/medium/low", "notes": "brief reason"}

Score this contact record's data quality based on:
- email_valid: {{email_valid}} (true/false)
- name_normalized: {{name_clean}} (cleaned/REVIEW)
- title_complete: {{title}} (not empty/empty)
- company_normalized: {{company}} (not empty/empty)
- is_duplicate: {{is_duplicate}} (true/false)
- freshness_likely: {{likely_current}} (true/false)

Rules:
- Start at 100
- Deduct 30 if email_valid is false
- Deduct 20 if is_duplicate is true
- Deduct 15 if name contains REVIEW
- Deduct 15 if title is empty
- Deduct 10 if freshness_likely is false
- Deduct 5 if company is empty

Return ONLY the final score (0-100).