docs update

PROJECT.md

@@ -27,3 +27,16 @@ Based on `resolve_scheme.md`:
 2) Implement alias/identifier indexes.
 3) Wire Wikidata calls + RDF storage using `atlas_store.py`.
 
+## Current status
+- `resolve()` basic loop is working end-to-end:
+  - store lookup by label/alias
+  - Wikidata quick-resolve fallback
+  - minimal persist to Virtuoso
+  - cache hits return stored results
+- Store lookup debug revealed a literal-filter mismatch; lookup is now split into label-first then alias (robust).
+- Wikidata resolution switched to the `wikidata.reconci.link` quick-resolve endpoint for consistent single-candidate resolution.
+- Maintenance scaffolding (dry-run CLI) is added:
+  - selects `atlas:needsCuration true`
+  - fetches Wikidata entity dump
+  - infers Atlas type buckets via `ontology/wikidata_subclassof.ttl` + keyword matching
+  - plans type-specific details (Person/Location today; others via buckets)

README.md

@@ -1,11 +1,19 @@
 # atlas2-mcp
 
-Python FastMCP server scaffold for the *atlas2* resolution flow.
+Python FastMCP server for the *atlas2* resolution flow.
 
-## Current status (v0)
+## Current status
 - Exposes **one tool**: `resolve()`
-- `resolve()` is intentionally stubbed and returns: `{ "status": "ok" }`
-- SPARQL/Virtuoso integration is scaffolded and prepared for the next iteration.
+- `resolve()` now performs the working path:
+  - store lookup (label/alias)
+  - Wikidata fallback using `wikidata.reconci.link` quick-resolve (single-candidate)
+  - minimal persist to Virtuoso via MCP
+  - cache hits return the stored result
+- Store lookup was debugged and made robust by splitting label-first then alias.
+- Maintenance scaffolding (dry-run CLI) exists to upgrade `atlas:needsCuration true` entities using:
+  - `ontology/wikidata_subclassof.ttl`
+  - Wikidata entity dumps
+  - Atlas type bucket inference (Person/Organization/Location/etc.)
 
 ## How to run
 
@@ -25,4 +33,3 @@ pip install -r requirements.txt
 ```
 
 Server runs on **port 8550** and mounts the MCP endpoint at **`/mcp`**.
-