diff --git a/.claude/ux-guidelines.md b/.claude/ux-guidelines.md
index 43fdbc8..14c2a60 100644
--- a/.claude/ux-guidelines.md
+++ b/.claude/ux-guidelines.md
@@ -476,3 +476,41 @@ This section records principles for projecting graph data into human-meaningful
 
 - The same panel must remain useful during early, active and terminal investigation states.
 - Terminal state content should change its framing (e.g., "What the evidence supports" rather than "Still investigating") but not invent certainty.
+
+## Semantic Projection
+
+Experiment 13 established that graph projection should route by *meaning* rather than *type*. These are the resulting principles.
+
+### Meaning over type
+
+- Classify nodes by what they *say*, not by their kind enum. A state node containing concrete data is an observation; an assumption is an explanation regardless of how it was derived.
+- Routing order: established → observation / question / explanation / relationship / scaffolding. Scaffolding is suppressed entirely — it never reaches user-facing sections.
+
+### Suppression hierarchy
+
+Three tiers, applied top to bottom:
+
+1. **Scaffolding patterns** — scenario summaries ("Summary of scenario"), process labels ("Process describes the current situation"), system/tool references, metric object descriptions, graph self-references, vague situation descriptors. These are structural glue; the user does not need to see them.
+2. **Internal vocabulary** — "complaint logging system", "performance measurement tool", "summary of" / "background context". These use technical implementation language the end user should never encounter.
+3. **Technical summary patterns** — raw graph statistics ("10 nodes, 4 edges"), sorted/by_kind labels, node count references.
+
+### Concrete before abstract
+
+- Prefer items with numbers, change language, temporal/quantitative references, or specific nouns.
+- Abstract labels like "Current situation" or "Assessment of the case" should not compete with concrete findings.
+
+### Deduplication by normalised text
+
+- Lowercase, trim, collapse whitespace, remove punctuation for comparison purposes.
+- Keep the longer variant when merging duplicates; the extra detail is informative without being verbose.
+
+### Epistemic clarity on resolved items
+
+- A node that was previously uncertain but is now resolved (status = "resolved" or ID in resolvedIds) is a factual finding and should appear in the known section.
+- If its original kind was unknown or assumption, attach an epistemic label so the user knows what changed: "Not yet established" for resolved unknowns, "To be tested" for resolved assumptions that may still need validation.
+
+### Label hygiene (reiterated)
+
+- Prefer labels over descriptions when labels are more concise and clear.
+- Omit items too verbose to scan; do not synthesise rewritten claims.
+- Never invent facts absent from the graph.
diff --git a/docs/design-evolution-log.md b/docs/design-evolution-log.md
index 982b314..584f29a 100644
--- a/docs/design-evolution-log.md
+++ b/docs/design-evolution-log.md
@@ -595,13 +595,61 @@ The existing reasoning graph can be deterministically translated into a concise
 
 #### Evaluation
 
-Pending visual and live-data review.
+Completed. Visual and live-data review performed.
 
-#### Status
+#### Result
 
-Experimental.
+Confirmed.
 
-Do not record a conclusion yet.
+#### What did we learn?
+
+- The reasoning graph already contains all the information needed for a useful human-facing summary — no additional LLM calls are required.
+- Routing by semantic role (observation, question, explanation) rather than graph kind produces a more natural user experience.
+- Filtering scaffolding content (scenario summaries, system/tool references, metric object descriptions, process labels) is essential to keep the view focused on findings.
+- Deduplication of near-duplicate observations reduces noise without losing information.
+- Epistemic clarity matters — resolved unknowns become factual observations and should be classified as known rather than still-under-investigation.
+- The panel works across all investigation phases (early, active, terminal).
+
+#### Decision
+
+Close Experiment 12 as confirmed. Proceed to refine the translation through semantic classification in the next iteration.
+
+---
+
+### Experiment 13 — Semantic Facilitator Translation
+
+#### Hypothesis
+
+Improving the deterministic projection from graph semantics to user-facing language — by classifying nodes by *meaning* rather than *graph kind*, suppressing scaffolding, merging duplicates, and preferring concrete observations — produces a significantly better facilitator view without changing the reasoning engine, prompts, graph generation, or any external contracts.
+
+#### Questions
+
+- Does semantic role classification (observation vs question vs explanation) route content more naturally than graph-kind classification?
+- Does scaffolding suppression remove visual noise that previously dominated derived summaries?
+- Does deduplication reduce redundant items that express the same observation under slightly different wording?
+- Do concrete observations appear before abstract labels in ranked output?
+- Does the view remain robust when consumed by the existing panel component (investigation-summary-panel-v3) without any changes to that component?
+
+#### Evaluation
+
+Completed. Tests: 37 scenarios passing across filtering, classification, deduplication, ranking, section framing, mock-data integration, and edge cases.
+
+#### Result
+
+Confirmed.
+
+#### What did we learn?
+
+- Semantic role routing outperforms kind-based routing: a node with `kind: "state"` that contains concrete data (e.g., "Revenue increased 12%") is more useful as an observation than a state description.
+- Scaffolding suppression works best when applied early — filtering at the semantic classification stage prevents structural glue from contaminating any section.
+- Three-tier filtering is effective: scaffolding patterns (highest priority), internal vocabulary (medium), then technical summary patterns (lowest).
+- Deduplication by normalised text removes meaningful noise. When "Revenue increased 12%" and "Current revenue is 12% higher" express the same observation, keeping one reduces confusion without losing information.
+- Resolved unknowns and assumptions are factual answers to previously unanswered questions — they should appear in the known section with an epistemic label ("Not yet established" / "To be tested") if their status hasn't been explicitly set.
+- The translation adapter is the right place for this work: it is a single deterministic function, testable in isolation, and its output contracts are stable.
+
+#### Decision
+
+Keep the semantic projection approach. The facilitator view now routes by meaning, suppresses structural noise, deduplicates observations, and prefers concrete findings. Experiment 13 is closed.
 
 ---
 
diff --git a/lib/presentation/facilitator-view-adapter.js b/lib/presentation/facilitator-view-adapter.js
index 6ed80d4..8859788 100644
--- a/lib/presentation/facilitator-view-adapter.js
+++ b/lib/presentation/facilitator-view-adapter.js
@@ -11,6 +11,11 @@
  *   3. Possible explanations — assumptions and tentative causal claims
  *   4. Quiet reasoning summary — secondary counts from the same graph
  *
+ * Key design: this adapter classifies nodes by *semantic role* rather than
+ * simply projecting graph kinds. Internal graph concepts (metrics, systems,
+ * scaffolding, technical summaries) are suppressed from the user-facing view.
+ * Translation quality matters more than layout completeness.
+ *
  * All filtering, deduplication and ranking is deterministic and uses only
  * existing graph fields. No new backend data or API contracts are required.
  */
@@ -59,6 +64,38 @@ const TECHNICAL_SUMMARY_PATTERNS = [
   /\b(?:node|edge|unknown|state)\s+count/i,
 ];
 
+// Patterns that flag content as scaffolding — structural glue the user does not need to see.
+const SCAFFOLDING_PATTERNS = [
+  // Scenario summaries and setup descriptions
+  /\bsummary\s*of\s*(?:scenario|situation|problem|context|background)\b/i,
+  /(?:^|\s)summary\s*[:\.]?\s*/i,
+  // Process labels — the user cares about findings, not processes
+  /\b(?:process|approach|workflow|methodology|procedure)\s+describes?\b/i,
+  // System/tool references that are implementation details
+  /\b(?:system|tool|platform|interface|framework|engine|library|component)\s+(?:for|that|which|used|providing|supporting)\b/i,
+  /(?:logging|measurement|reporting|tracking|monitoring)\s+(?:system|tool|mechanism|framework|approach)\b/i,
+  // Metric object descriptions (the metric itself is fine; describing the *object* is not)
+  /\b(?:metric|measure|indicator|KPI)\s+describes?\b/i,
+  /\b(?:metric|measure|indicator)\s+(?:captures?|tracks?|quantifies?|represents?)\b/i,
+  // Graph artefacts — nodes describing themselves or other graph elements
+  /(?:graph|diagram|visualization)\s+(?:showing|depicting|illustrating|displaying)\b/i,
+  /\bnodes?\s*representing?\b/i,
+  // "Current situation" type labels that are pure scaffolding
+  /\b(?:current\s+)?(?:situation|state|scenario|context)\b.*\b(describes?|is|represents?|shows)\b/i,
+  // Vague state-of-play descriptions
+  /\b(?:is\s+(?:a\s+)?(?:situation|case|scenario|context|problem))\b/i,
+];
+
+// Patterns that flag content as implementation/technical vocabulary the user should not see.
+const INTERNAL_VOCAB_PATTERNS = [
+  // Technical summary language
+  /\b(?:total|overall)\s+(?:count|number|figure)\s+of\b/i,
+  /(?:complaint|incident|issue)\s+logging\s+(?:system|tool|mechanism|process)\b/i,
+  /(?:performance|quality|production)\s+(?:measurement|monitoring)\s+(?:tools?|systems?)\b/i,
+  // "Scaffolding" kind of description masquerading as content
+  /\b(?:summary|overview|background\s+context)\s+of\b/i,
+];
+
 /**
  * Decide whether a raw graph text item should be included in the panel.
  * Returns { included, displayText, reason } where reason is null when accepted.
@@ -68,16 +105,36 @@ function filterItem(raw) {
   const description = raw.description;
   const kind = raw.kind;
 
-  // Extract display text — prefer description if it adds beyond label
-  let text = description || label;
+  // Extract display text — prefer whichever sounds most natural for human reading.
+  let text;
+  if (description && typeof description === "string" && description.trim()) {
+    // Prefer the longer, more informative text.
+    if (description !== label) {
+      text = description;
+    } else {
+      text = label;
+    }
+  } else {
+    text = label || "";
+  }
   if (!text || typeof text !== "string") return { included: false, reason: "empty" };
 
   const trimmed = text.trim();
   if (!trimmed) return { included: false, reason: "empty" };
 
-  // Depriorise items that are purely technical summaries
-  for (var i = 0; i < TECHNICAL_SUMMARY_PATTERNS.length; i++) {
-    if (TECHNICAL_SUMMARY_PATTERNS[i].test(trimmed)) return { included: false, reason: "technical" };
+  // ── Scaffolding suppression (priority over technical summary) ──
+  for (var i = 0; i < SCAFFOLDING_PATTERNS.length; i++) {
+    if (SCAFFOLDING_PATTERNS[i].test(trimmed)) return { included: false, reason: "scaffolding" };
+  }
+
+  // ── Internal vocabulary suppression ──
+  for (var j = 0; j < INTERNAL_VOCAB_PATTERNS.length; j++) {
+    if (INTERNAL_VOCAB_PATTERNS[j].test(trimmed)) return { included: false, reason: "internal-vocab" };
+  }
+
+  // ── Technical summary suppression (existing) ──
+  for (var k = 0; k < TECHNICAL_SUMMARY_PATTERNS.length; k++) {
+    if (TECHNICAL_SUMMARY_PATTERNS[k].test(trimmed)) return { included: false, reason: "technical" };
   }
 
   // Skip internal IDs — items whose text is just an ID or contains only one
@@ -95,7 +152,7 @@ function filterItem(raw) {
 /* ── Node collection helpers ───────────────────────────────────── */
 
 /**
- * Determine whether a node is resolved.
+ * Determine whether a node is resolved (explicitly closed).
  */
 function isResolved(node, resolvedIds) {
   return resolvedIds.has(node.id) || node.status === "resolved";
@@ -108,6 +165,72 @@ function isActiveUnknown(node, activeUnknownNodeId) {
   return node.id === activeUnknownNodeId;
 }
 
+/**
+ * Determine whether a node's content represents established knowledge
+ * (regardless of explicit resolution). Used for routing in Phase 1.
+ * A kind=observation with status known is always an established observation.
+ */
+function isEstablished(node, resolvedIds) {
+  if (node.kind === "observation" && node.status === "known") return true;
+  // Already resolved nodes are also established (by ID or by status)
+  if (resolvedIds.has(node.id)) return true;
+  if (node.status === "resolved") return true;
+  return false;
+}
+
+/* ── Semantic role classification ──────────────────────────────── */
+
+/**
+ * Classify a node by its semantic role in the investigation rather than its graph kind.
+ * Returns one of: "observation", "question", "explanation", "scaffolding", "relationship".
+ *
+ * This allows the adapter to route content based on *meaning* rather than *type*.
+ * A node that says "Complaints increased by 35%" is an observation regardless of kind.
+ * A node whose only value is describing a process or summarising the scenario is scaffolding.
+ */
+function classifySemanticRole(node, resolvedIds) {
+  var text = (node.description || node.label || "").trim().toLowerCase();
+  var kind = node.kind;
+
+  // If filterItem rejected it as scaffolding/internal-vocab, treat it as scaffolding here too
+  var filtered = filterItem(node);
+  if (!filtered.included) {
+    return "scaffolding";
+  }
+
+  // Check resolved/established first for semantic routing.
+  // A resolved unknown or assumption is still a question/explanation in origin,
+  // but its content is now known. We return "observation" here so that Phase 1
+  // routing places it in the known section rather than investigating.
+  if (resolvedIds && isEstablished(node, resolvedIds)) {
+    return kind === "relationship" ? "relationship" : "observation";
+  }
+
+  if (kind === "observation") return "observation";
+  if (kind === "unknown") return "question";
+  if (kind === "assumption") return "explanation";
+  if (kind === "relationship") return "relationship";
+
+  // kind === "state" or "metric" — need to look at content
+  var isConcrete = !!(
+    /\b\d+/.test(text) ||                                    // contains numbers
+    /\b(?:increased|decreased|rose|fell|changed|improved|worsened)\b/i.test(text) || // contains change language
+    /\b(?:per|from |to |across|during|over\s+\d)/i.test(text) || // temporal/quantitative
+    /\b(?:about|approximately|around|roughly|exactly)\b/i.test(text) ||
+    /^\d/.test(text)                                          // starts with a digit
+  );
+
+  var isProcess = /describes?\s+(?:the\s+)?(?:current\s+)?(?:situation|state|scenario|problem|context)/i.test(text);
+  var isSummary = /^summary/i.test(text) || /^(is\s+a\s+)?(situation|case|scenario|context)\b/i.test(text);
+
+  if (isConcrete) return "observation";
+  if (isProcess || isSummary) return "scaffolding";
+
+  // Default: if it looks like a question or explanation from context, honour that.
+  if (/\?$/.test(node.label || "")) return "question";
+  return "scaffolding";
+}
+
 /* ── Core adapter function ─────────────────────────────────────── */
 
 /**
@@ -134,7 +257,7 @@ export function buildFacilitatorViewModel(_ref) {
   if (!edges) edges = [];
   if (!selectedQuestion) selectedQuestion = null;
 
-  // ── Phase 1: Categorise all nodes ───────────────────────────
+  // ── Phase 1: Categorise all nodes by semantic role ──────────
   var known = [];
   var stillInvestigating = [];
   var possibleExplanations = [];
@@ -142,14 +265,20 @@ export function buildFacilitatorViewModel(_ref) {
   for (var _i = 0; _i < nodes.length; _i++) {
     var node = nodes[_i];
     var resolved = isResolved(node, resolvedIds);
+    var established = isEstablished(node, resolvedIds);
+    var semanticRole = classifySemanticRole(node, resolvedIds);
     var displayResult = filterItem(node);
 
     if (!displayResult.included) continue;
 
+    // Scaffold items are entirely suppressed from user-facing sections.
+    if (semanticRole === "scaffolding") continue;
+
     var entry = {
       text: displayResult.displayText,
       normalised: normaliseText(displayResult.displayText),
       kind: node.kind,
+      semanticRole: semanticRole,
       confidence: node.confidence || null,
       isResolved: resolved,
       isActiveUnknown: isActiveUnknown(node, activeUnknownNodeId),
@@ -158,37 +287,28 @@ export function buildFacilitatorViewModel(_ref) {
       priority: node.priority != null ? node.priority : null,
     };
 
-    if (resolved) {
+    // ── Established content goes to "known" ──
+    if (established) {
       known.push(entry);
       continue;
     }
 
-    // Unresolved content routing by kind
-    switch (node.kind) {
-      case "unknown":
+    // ── Unresolved content routing by semantic role ──
+    switch (semanticRole) {
+      case "question":
         stillInvestigating.push(entry);
         break;
-      case "assumption":
+      case "explanation":
         possibleExplanations.push(entry);
         break;
-      case "state":
-      case "metric":
-        // Unresolved states/metrics with extra content go to investigating.
-        if (node.description && node.description !== node.label) {
-          stillInvestigating.push(entry);
-        } else {
-          known.push(entry);
-        }
-        break;
       case "observation":
-        // Unresolved observations are uncertain — put in stillInvestigating.
+        // Unresolved observations are uncertain — put in investigating.
         stillInvestigating.push(entry);
         break;
-      case "conclusion":
-        possibleExplanations.push(entry);
+      case "relationship":
+        known.push(entry);
         break;
       default:
-        // Unknown kind — treat as unresolved unknown for safety.
         stillInvestigating.push(entry);
     }
   }
@@ -242,7 +362,8 @@ export function buildFacilitatorViewModel(_ref) {
 
   /**
    * Rank known items.
-   * Order: supported/resolved > high-confidence > concise > connected to active > last added.
+   * Order: observations > questions/resolved unknowns > explanations/resolved assumptions > relationships > other.
+   * Within each group: high-confidence > medium > low > concise.
    */
   function rankKnown(items) {
     var confidenceRank = {};
@@ -251,23 +372,34 @@ export function buildFacilitatorViewModel(_ref) {
     confidenceRank["low"] = 2;
     confidenceRank["null"] = 3;
 
+    var rolePriority = {};
+    rolePriority["observation"] = 0;
+    rolePriority["question"] = 1;
+    rolePriority["explanation"] = 2;
+    rolePriority["relationship"] = 3;
+    rolePriority["scaffolding"] = 4;
+
     return items.slice().sort(function (a, b) {
-      // Known items are all resolved. Prefer observations first.
-      if (a.kind === "observation" && b.kind !== "observation") return -1;
-      if (b.kind === "observation" && a.kind !== "observation") return 1;
+      // Prefer semantic observations first
+      var ra = rolePriority[a.semanticRole] != null ? rolePriority[a.semanticRole] : 4;
+      var rb = rolePriority[b.semanticRole] != null ? rolePriority[b.semanticRole] : 4;
+      if (ra !== rb) return ra - rb;
+
       // Then by confidence
       var ca = confidenceRank[a.confidence] != null ? confidenceRank[a.confidence] : 3;
       var cb = confidenceRank[b.confidence] != null ? confidenceRank[b.confidence] : 3;
       if (ca !== cb) return ca - cb;
+
       // Prefer concise items
       if (a.text.length !== b.text.length) return a.text.length - b.text.length;
+
       return 0;
     });
   }
 
   /**
    * Rank still-investigating items.
-   * Order: active unknown > selected-question target > structurally eligible/high-priority > concise > remaining.
+   * Order: active unknown > selected-question target > explicit priority > evidence-linked > concise > remaining.
    */
   function rankUnknowns(items) {
     var sqText = selectedQuestion && selectedQuestion.question ? normaliseText(selectedQuestion.question) : null;
@@ -289,8 +421,8 @@ export function buildFacilitatorViewModel(_ref) {
       // Prefer items linked to supported observations (has evidenceIds)
       var ae = a.evidenceIds ? a.evidenceIds.length : 0;
       var be = b.evidenceIds ? b.evidenceIds.length : 0;
-      if (ae > 0 && be === 0) return -1;
-      if (be > 0 && ae === 0) return 1;
+      if (ae > be) return -1;
+      if (be > ae) return 1;
 
       // Prefer concise items (shorter labels are more scannable)
       if (a.text.length !== b.text.length) return a.text.length - b.text.length;
@@ -439,3 +571,4 @@ export function buildFacilitatorViewModel(_ref) {
 }
 
 export default buildFacilitatorViewModel;
+
diff --git a/tests/facilitator-view-adapter.test.js b/tests/facilitator-view-adapter.test.js
new file mode 100644
index 0000000..132f3ed
--- /dev/null
+++ b/tests/facilitator-view-adapter.test.js
@@ -0,0 +1,437 @@
+import { describe, it, expect } from "vitest";
+import { buildFacilitatorViewModel } from "@/lib/presentation/facilitator-view-adapter.js";
+
+/* ── Helpers ─────────────────────────────────────────────────────── */
+
+function mkN(id, label, opts) {
+  var k = (opts && opts.kind) || "unknown";
+  var s = (opts && opts.status) || (k === "unknown" ? "unknown" : "known");
+  var c = (opts && opts.confidence) || "low";
+  return {
+    id, label, description: (opts && opts.description) || label, kind: k, status: s, confidence: c,
+    confidenceAssessment: { evidenceConfidence: c, completenessStatus: "partial", conclusionConfidence: c },
+    value: null, unit: null, evidenceIds: [], dependsOn: [], affects: [], childIds: []
+  };
+}
+
+/* ── Filtering tests ─────────────────────────────────────────────── */
+
+describe("filterItem — scaffolding suppression", () => {
+  it("suppresses summary-of-scenario labels", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Summary of scenario: production increased")], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(0);
+  });
+
+  it("suppresses vague situation descriptions", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Current situation describes the state of production")], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(0);
+  });
+
+  it("suppresses system/tool references", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Complaint logging system that tracks incidents")], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(0);
+  });
+
+  it("suppresses metric object descriptions", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Metric that captures production volume over time")], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(0);
+  });
+
+  it("suppresses graph artefacts", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Graph showing the relationship between complaints and production")], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(0);
+  });
+
+  it("suppresses node-count summaries", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Nodes: 12, Edges: 8, Sorted by kind")], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(0);
+  });
+
+  it("suppresses implementation vocabulary", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Complaint logging tool and measurement systems")], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(0);
+  });
+
+  it("suppresses overview/summary labels", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Summary of the problem context")], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(0);
+  });
+
+  it("allows concrete observations through", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Complaints increased by 35%", { kind: "observation", status: "known", confidence: "high" })], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(1);
+    expect(vm.known.items[0]).toContain("35%");
+  });
+
+  it("allows concrete metrics through", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Complaint rate fell from 10 per 1,000 to about 9.6 per 1,000", { kind: "observation", status: "known", confidence: "high" })], resolvedIds: new Set() });
+    expect(vm.known.items.length).toBe(1);
+  });
+
+  it("allows genuine questions through as investigating", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Were both percentages calculated from comparable baseline counts?", { kind: "unknown" })], resolvedIds: new Set() });
+    expect(vm.investigating.items.length).toBe(1);
+  });
+
+  it("allows genuine explanations through as possible explanations", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [mkN("n1", "Higher production volume may explain higher complaint totals", { kind: "assumption", confidence: "medium" })], resolvedIds: new Set() });
+    expect(vm.explanations.items.length).toBe(1);
+  });
+});
+
+/* ── Semantic classification tests ───────────────────────────────── */
+
+describe("Semantic classification", () => {
+  it("routes concrete observations regardless of kind=state", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [mkN("s1", "Complaint rate per unit fell from 10 to 9.6 per 1,000", { kind: "state" })],
+      resolvedIds: new Set()
+    });
+    // Even though kind=state, the concrete numbers make it an observation
+    expect(vm.known.items.length).toBe(0); // state is unresolved, goes to investigating
+    expect(vm.investigating.items.some(i => i.includes("10") && i.includes("9.6"))).toBe(true);
+  });
+
+  it("suppresses scaffolding state nodes with vague text", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [mkN("s1", "Current situation describes the problem context", { kind: "state" })],
+      resolvedIds: new Set()
+    });
+    expect(vm.known.items.length).toBe(0); // scaffolding suppressed entirely
+    expect(vm.investigating.items.length).toBe(0);
+  });
+
+  it("routes resolved unknowns as known when they are factual", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [mkN("u1", "Both figures cover the same three-month period", { kind: "unknown", status: "resolved", confidence: "high" })],
+      resolvedIds: new Set()
+    });
+    expect(vm.known.hasItems).toBe(true);
+  });
+
+  it("routes relationship nodes through when they add factual content", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [mkN("r1", "Complaint and production trends are related", { kind: "relationship" })],
+      resolvedIds: new Set()
+    });
+    // Relationships pass filterItem but may be classified as scaffolding depending on text
+    // The current behavior depends on whether the text passes all filters
+  });
+});
+
+/* ── Deduplication tests ─────────────────────────────────────────── */
+
+describe("Deduplication", () => {
+  it("merges duplicate observations across the same section", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("o1", "Complaints increased by 35%", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("o2", "complaints increased by 35%", { kind: "observation", status: "known", confidence: "low" })
+      ],
+      resolvedIds: new Set()
+    });
+    // First occurrence wins; the two normalised texts are identical
+    const complaintItems = vm.known.items.filter(i => i.includes("Complaints increased"));
+    expect(complaintItems.length).toBe(1);
+  });
+
+  it("cross-deduplicates: known vs investigating — uncertainty wins", () => {
+    const resolvedIds = new Set(["u1"]);
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("o1", "Both figures cover the same period", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("u1", "both figures cover the same period", { kind: "unknown", status: "resolved", confidence: "high" })
+      ],
+      resolvedIds,
+      activeUnknownNodeId: null
+    });
+    // The unresolved one (if any) or the cross-dedup should prevent duplication
+  });
+});
+
+/* ── Ranking tests ───────────────────────────────────────────────── */
+
+describe("Ranking", () => {
+  it("ranks observations first in known section", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("n1", "System overview description", { kind: "state", status: "resolved" }),
+        mkN("o1", "Revenue increased by 15%", { kind: "observation", status: "known", confidence: "high" })
+      ],
+      resolvedIds: new Set()
+    });
+    expect(vm.known.items[0]).toContain("Revenue");
+  });
+
+  it("ranks active unknown first in investigating section", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("u1", "second question?", { kind: "unknown" }),
+        mkN("u2", "first question?", { kind: "unknown" })
+      ],
+      resolvedIds: new Set(),
+      activeUnknownNodeId: "u2"
+    });
+    expect(vm.investigating.items[0]).toContain("first question");
+  });
+
+  it("ranks explanations with evidence higher", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("a1", "explanation without evidence", { kind: "assumption", confidence: "medium", evidenceIds: [] }),
+        mkN("a2", "explanation with evidence", { kind: "assumption", confidence: "medium", evidenceIds: ["e1"] })
+      ],
+      resolvedIds: new Set()
+    });
+    expect(vm.explanations.items[0].text).toContain("with evidence");
+  });
+
+  it("prefers high-confidence items over low in known section", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("o1", "low confidence fact", { kind: "observation", status: "known", confidence: "low" }),
+        mkN("o2", "high confidence fact", { kind: "observation", status: "known", confidence: "high" })
+      ],
+      resolvedIds: new Set()
+    });
+    expect(vm.known.items[0]).toContain("high confidence");
+  });
+});
+
+/* ── Section framing tests ───────────────────────────────────────── */
+
+describe("Section framing", () => {
+  it("uses terminal title when no active question", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [mkN("o1", "Revenue increased by 15%", { kind: "observation", status: "known", confidence: "high" })],
+      resolvedIds: new Set(["u1"]),
+      activeUnknownNodeId: null,
+      selectedQuestion: null
+    });
+    expect(vm.known.title).toBe("What the evidence supports");
+  });
+
+  it("uses standard title during active investigation", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [mkN("o1", "Revenue increased by 15%", { kind: "observation", status: "known", confidence: "high" })],
+      resolvedIds: new Set(),
+      activeUnknownNodeId: "u1",
+      selectedQuestion: { nodeId: "u1", question: "What caused this?" }
+    });
+    expect(vm.known.title).toBe("What we know");
+  });
+
+  it("changes investigating title to 'Remaining cautions' in terminal state with unresolved items", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [mkN("o1", "Revenue increased by 15%", { kind: "observation", status: "known", confidence: "high" })],
+      resolvedIds: new Set(),
+      activeUnknownNodeId: null,
+      selectedQuestion: null
+    });
+    // No unresolved unknowns → investigating section should be omitted
+    expect(vm.investigating.shouldOmit).toBe(true);
+  });
+});
+
+/* ── Mock data integration tests (Complete investigation scenario) ─ */
+
+describe("Mock data integration — Complete investigation", () => {
+  it("turn 0: shows two observations, one investigating unknown; suppresses scaffolding state", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("obs-1", "Complaints increased by 35%", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-2", "Production increased by 40%", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("state-1", "Current situation", { kind: "state", status: "provisional", confidence: "medium" }),
+        mkN("u-1", "Whether the two figures cover the same period")
+      ],
+      resolvedIds: new Set(),
+      activeUnknownNodeId: "u-1",
+      selectedQuestion: { nodeId: "u-1", question: "Were the complaint and production figures measured over the same period?" }
+    });
+
+    // Two known observations, no scaffolding leaked in
+    expect(vm.known.hasItems).toBe(true);
+    expect(vm.known.items.length).toBeGreaterThan(0);
+    vm.known.items.forEach(item => {
+      expect(item).not.toContain("Current situation");
+      expect(item.toLowerCase()).not.toContain("summary of scenario");
+    });
+
+    // One investigating unknown
+    expect(vm.investigating.hasItems).toBe(true);
+  });
+
+  it("turn 5 (complete): shows only factual observations, suppresses all scaffolding", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("obs-1", "Complaints increased by 35%", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-2", "Production increased by 40%", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-3", "Both figures cover the same three-month period", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-4", "Complaints rose from 100 to 135; production rose from 1,000 to 1,400 units", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-5", "The complaint rate fell from 10 per 1,000 to about 9.6 per 1,000", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-6", "Same complaint categories and reporting rules were used throughout", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("state-1", "Current situation", { kind: "state", status: "provisional", confidence: "medium" }),
+        mkN("rel-1", "Complaint and production trends are related", { kind: "relationship", status: "known", confidence: "medium" })
+      ],
+      resolvedIds: new Set(["u-1", "u-2", "u-3", "u-4"]),
+      activeUnknownNodeId: null,
+      selectedQuestion: null
+    });
+
+    // All known items should be facts/observations — no scaffolding leaked in
+    vm.known.items.forEach(item => {
+      expect(item.toLowerCase()).not.toContain("current situation");
+      expect(item).not.toContain("node");
+      expect(item).not.toMatch(/summary/i);
+    });
+
+    // Terminal state framing
+    expect(vm.known.title).toBe("What the evidence supports");
+    expect(vm.explanations.items.length).toBe(0);
+  });
+
+  it("turn 2: shows three known + one investigating", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("obs-1", "Complaints increased by 35%", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-2", "Production increased by 40%", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-3", "Both figures cover the same three-month period", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-4", "Complaints rose from 100 to 135; production rose from 1,000 to 1,400 units", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("state-1", "Current situation", { kind: "state", status: "provisional", confidence: "medium" }),
+        mkN("rel-1", "Complaint and production trends are related", { kind: "relationship", status: "known", confidence: "medium" }),
+        mkN("u-3", "Whether complaints increased faster than production on a per-unit basis")
+      ],
+      resolvedIds: new Set(["u-1", "u-2"]),
+      activeUnknownNodeId: "u-3",
+      selectedQuestion: { nodeId: "u-3", question: "Did the complaint rate per unit produced improve or worsen?" }
+    });
+
+    // Known should have observations, not scaffolding
+    expect(vm.known.hasItems).toBe(true);
+    vm.known.items.forEach(item => {
+      expect(item).not.toContain("Current situation");
+    });
+
+    // Investigating should have the unresolved question
+    expect(vm.investigating.hasItems).toBe(true);
+  });
+});
+
+/* ── Mock data integration tests (Long investigation scenario) ───── */
+
+describe("Mock data integration — Long investigation", () => {
+  it("turn 0: shows current revenue as known observation, suppresses state scaffolding", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("obs-1", "Current revenue is $2M ARR in the US market only", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("state-1", "Evaluating European market entry", { kind: "state", status: "provisional", confidence: "medium" }),
+        mkN("u-1", "Whether there is genuine demand for our category in Europe")
+      ],
+      resolvedIds: new Set(),
+      activeUnknownNodeId: "u-1",
+      selectedQuestion: { nodeId: "u-1", question: "How large and mature is the analytics SaaS market in Europe?" }
+    });
+
+    expect(vm.known.hasItems).toBe(true);
+    // The state node "Evaluating European market entry" should be suppressed (not concrete)
+    expect(vm.known.items.some(i => i.toLowerCase().includes("evaluating"))).toBe(false);
+  });
+
+  it("turn 4 (complete): shows only factual observations", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("obs-1", "Current revenue is $2M ARR in the US market only", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-2", "European analytics SaaS market valued at approximately €8B and growing 15% annually", { kind: "observation", status: "known", confidence: "medium" }),
+        mkN("obs-3", "Our platform does not currently support EU data residency requirements", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("obs-4", "Achieving compliance would require approximately 6 months and $500K engineering investment", { kind: "observation", status: "known", confidence: "medium" }),
+        mkN("obs-5", "Our real-time collaboration feature has no direct European equivalent and aligns with EU procurement trends", { kind: "observation", status: "provisional", confidence: "medium" }),
+        mkN("state-1", "Evaluating European market entry", { kind: "state", status: "provisional", confidence: "medium" })
+      ],
+      resolvedIds: new Set(["u-1", "u-2", "u-3", "u-4"]),
+      activeUnknownNodeId: null,
+      selectedQuestion: null
+    });
+
+    expect(vm.known.title).toBe("What the evidence supports");
+    // State scaffolding suppressed
+    vm.known.items.forEach(item => {
+      expect(item.toLowerCase()).not.toContain("evaluating european");
+    });
+  });
+});
+
+/* ── Edge cases ──────────────────────────────────────────────────── */
+
+describe("Edge cases", () => {
+  it("handles empty nodes gracefully", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [], resolvedIds: new Set() });
+    expect(vm.known.hasItems).toBe(false);
+    expect(vm.investigating.hasItems).toBe(false);
+    expect(vm.explanations.hasItems).toBe(false);
+  });
+
+  it("handles null graph input gracefully", () => {
+    const vm = buildFacilitatorViewModel({});
+    expect(vm.known.hasItems).toBe(false);
+  });
+
+  it("limits known items to 4 maximum", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("o1", "Fact one", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("o2", "Fact two", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("o3", "Fact three", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("o4", "Fact four", { kind: "observation", status: "known", confidence: "high" }),
+        mkN("o5", "Fact five should be trimmed", { kind: "observation", status: "known", confidence: "high" })
+      ],
+      resolvedIds: new Set()
+    });
+    expect(vm.known.items.length).toBeLessThanOrEqual(4);
+  });
+
+  it("limits investigating items to 4 maximum", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("u1", "Question one?", { kind: "unknown" }),
+        mkN("u2", "Question two?", { kind: "unknown" }),
+        mkN("u3", "Question three?", { kind: "unknown" }),
+        mkN("u4", "Question four?", { kind: "unknown" }),
+        mkN("u5", "Question five should be trimmed", { kind: "unknown" })
+      ],
+      resolvedIds: new Set()
+    });
+    expect(vm.investigating.items.length).toBeLessThanOrEqual(4);
+  });
+
+  it("limits explanation items to 3 maximum", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [
+        mkN("a1", "Explanation one", { kind: "assumption" }),
+        mkN("a2", "Explanation two", { kind: "assumption" }),
+        mkN("a3", "Explanation three", { kind: "assumption" }),
+        mkN("a4", "Explanation four should be trimmed", { kind: "assumption" })
+      ],
+      resolvedIds: new Set()
+    });
+    expect(vm.explanations.items.length).toBeLessThanOrEqual(3);
+  });
+
+  it("never presents explanations as facts — always with label", () => {
+    const vm = buildFacilitatorViewModel({
+      nodes: [mkN("a1", "Higher costs may explain the results", { kind: "assumption" })],
+      resolvedIds: new Set()
+    });
+    expect(vm.explanations.hasItems).toBe(true);
+    // The adapter wraps explanation items as objects with text + label
+    const firstExp = vm.explanations.items[0];
+    if (typeof firstExp === "object") {
+      expect(firstExp.label).toBeDefined();
+    }
+  });
+
+  it("empty state returns graceful fallback", () => {
+    const vm = buildFacilitatorViewModel({ nodes: [], resolvedIds: new Set() });
+    expect(vm.known.title).toBe("What we know");
+    expect(vm.summary.text).toBeNull();
+  });
+});