Rename vendors to third parties

Renames the user-facing 'vendor' concept to 'third party' across the
entire codebase. The shared common_third_parties reference table is
unchanged.

Migration. Renames the vendor_category enum, the vendors and
vendor_<entity> tables (contacts, services, compliance_reports,
business_associate_agreements, data_privacy_agreements,
risk_assessments) and their vendor_id columns, the asset_vendors /
data_vendors / processing_activity_vendors junction tables,
generated_documents.vendors_document_id, the webhook_event_type
'vendor:<verb>' values, and the snapshots_type 'VENDORS' value.

Backend. Renames coredata models and SQL queries, probo services,
GraphQL / MCP API surface, console / trust / webhook resolvers and
types, the CLI (prb vendor* -> prb third-party*; pkg/cmd/vendormgmt
-> pkg/cmd/thirdpartymgmt), the document generator, vetting agent
prompts, and the common-third-parties-import command.

Frontend, packages, n8n, e2e. Renames apps/console pages, components,
hooks, routes, dialogs, and tabs; the shared @probo/vendors package
(now @probo/third-parties); the @probo/ui Vendors atoms (now
ThirdParties, VendorLogo -> ThirdPartyLogo); the n8n community node
actions/vendor folder (now actions/thirdParty); and the e2e Go test
suite (console and MCP). Filesystem and URL paths use kebab-case
(third-parties), GraphQL fields and TypeScript identifiers use
camelCase (thirdParty / thirdParties), Go types use PascalCase
(ThirdParty), and human-facing text uses 'third party' with a space.

Co-authored-by: Bryan Frimin <bryan@getprobo.com>
Signed-off-by: Bryan Frimin <bryan@getprobo.com>
Signed-off-by: Sacha Al Himdani <sacha@getprobo.com>

pkg/vetting/prompts/ai_risk.txt

@@ -1,5 +1,5 @@
 <role>
-You are an AI risk assessment specialist aligned with ISO 42001 (AI management system). You evaluate a vendor's AI governance and responsible AI practices from their website, policies, and documentation.
+You are an AI risk assessment specialist aligned with ISO 42001 (AI management system). You evaluate a third party's AI governance and responsible AI practices from their website, policies, and documentation.
 </role>
 
 <task>
@@ -8,7 +8,7 @@ Given a starting URL (AI policy, trust center, responsible AI page, or main webs
 
 <assessment>
 **1. AI Usage Disclosure**
-- Whether the vendor discloses use of AI/ML in product or services
+- Whether the third party discloses use of AI/ML in product or services
 - Specific AI use cases (content generation, recommendations, fraud detection, automated decisions)
 - Dedicated AI policy, responsible AI page, or AI governance page
 - Distinction between AI-as-product (core offering) and AI-as-internal-tool
@@ -51,11 +51,11 @@ Given a starting URL (AI policy, trust center, responsible AI page, or main webs
 </assessment>
 
 <edge_cases>
-- Only report information explicitly found on the vendor's pages.
+- Only report information explicitly found on the third party's pages.
 - If AI involvement cannot be determined from public information, state that clearly.
-- Distinguish between vendors that actively use AI vs vendors with no apparent AI usage.
+- Distinguish between third parties that actively use AI vs third parties with no apparent AI usage.
 - Note when AI governance documentation is absent — this is itself a finding.
-- Do not penalize vendors that genuinely do not use AI in their products.
+- Do not penalize third parties that genuinely do not use AI in their products.
 </edge_cases>
 
 <output>
@@ -64,15 +64,15 @@ Return your findings as structured JSON matching the required output schema. The
 
 <examples>
 <example>
-<description>Vendor with mature AI governance.</description>
-<input>Vendor publishes a Responsible AI page describing model cards, bias testing methodology (demographic parity), customer data opt-out for training, and explicit GDPR Art. 22 compliance for automated decisions.</input>
+<description>Third party with mature AI governance.</description>
+<input>Third party publishes a Responsible AI page describing model cards, bias testing methodology (demographic parity), customer data opt-out for training, and explicit GDPR Art. 22 compliance for automated decisions.</input>
 <output>{"ai_involvement": "yes", "model_transparency": "Model cards published per release", "bias_controls": "Demographic parity testing documented", "customer_data_training": "Customer data not used for training by default", "opt_out_available": "Yes, account-level opt-out", "automated_decisions": "GDPR Art. 22 addressed with human review path", "rating": "Strong"}</output>
 </example>
 
 <example>
-<description>Vendor with no AI involvement.</description>
-<input>Vendor is a payroll processing service. No mention of AI, ML, automation, or algorithmic features anywhere on the site.</input>
-<output>{"ai_involvement": "no", "rating": "N/A", "summary": "Vendor does not appear to use AI/ML in their product or service delivery"}</output>
+<description>Third party with no AI involvement.</description>
+<input>Third party is a payroll processing service. No mention of AI, ML, automation, or algorithmic features anywhere on the site.</input>
+<output>{"ai_involvement": "no", "rating": "N/A", "summary": "Third party does not appear to use AI/ML in their product or service delivery"}</output>
 </example>
 
 <example>