Claimify

Claimify

Extract claims from text and map their logical relationships into structured argument networks.

Overview

Claimify transforms messy discourse (conversations, documents, debates, meeting notes) into analyzable claim structures that reveal:

• Explicit and implicit claims
• Logical relationships (supports/​opposes/​assumes/​contradicts)
• Evidence chains
• Argument structure
• Tension points and gaps

Workflow

1. Ingest: Read source material (conversation, document, transcript)
2. Extract: Identify atomic claims (one assertion per claim)
3. Classify: Label claim types (factual/​normative/​definitional/​causal/​predictive)
4. Map: Build relationship graph (which claims support/​oppose/​assume others)
5. Analyze: Identify structure, gaps, contradictions, implicit assumptions
6. Output: Format as requested (table/​graph/​narrative/​JSON)

Claim Extraction Guidelines

Atomic Claims

Each claim should be a single, testable assertion.

Good:

• "AI adoption increases productivity by 15-30%"
• "Psychological safety enables team learning"
• "Current training methods fail to build AI fluency"

Bad (not atomic):

• "AI is useful and everyone should use it" → Split into 2 claims

Claim Types

Relationship Types

• Supports: Claim A provides evidence/​reasoning for claim B
• Opposes: Claim A undermines or contradicts claim B
• Assumes: Claim A requires claim B to be true (often implicit)
• Refines: Claim A specifies/​clarifies claim B
• Contradicts: Claims are mutually exclusive
• Independent: No logical relationship

Output Formats

Table Format (default)

| ID | Claim | Type | Supports | Opposes | Assumes | Evidence |
|----|-------|------|----------|---------|---------|----------|
| C1 | [claim text] | Factual | - | - | C5 | [source/​reasoning] |
| C2 | [claim text] | Normative | C1 | C4 | - | [source/​reasoning] |

Graph Format

Use Mermaid for visualization:

graph TD
    C1[Claim 1: AI increases productivity]
    C2[Claim 2: Training is insufficient]
    C3[Claim 3: Organizations should invest]
    
    C1 -->|supports| C3
    C2 -->|supports| C3
    C2 -.->|assumes| C4[Implicit: Change requires structure]

Narrative Format

Write as structured prose with clear transitions showing logical flow:

## Core Argument

The author argues that [main claim]. This rests on three supporting claims:

1. [Factual claim] - This is supported by [evidence]
2. [Causal claim] - However, this assumes [implicit assumption]
3. [Normative claim] - This follows if we accept [prior claims]

## Tensions

The argument contains internal tensions:
- Claims C2 and C5 appear contradictory because...
- The causal chain from C3→C7 has a missing premise...

JSON Format

For programmatic processing:

{
  "claims": [
    {
      "id": "C1",
      "text": "AI adoption increases productivity",
      "type": "factual",
      "explicit": true,
      "supports": ["C3"],
      "opposed_by": [],
      "assumes": ["C4"],
      "evidence": "Multiple case studies cited"
    }
  ],
  "relationships": [
    {"from": "C1", "to": "C3", "type": "supports", "strength": "strong"}
  ],
  "meta_analysis": {
    "completeness": "Missing link between C2 and C5",
    "contradictions": ["C4 vs C7"],
    "key_assumptions": ["C4", "C9"]
  }
}

Analysis Depth Levels

Level 1: Surface

• Extract only explicit claims
• Basic support/​oppose relationships
• No implicit assumption mining

Level 2: Standard (default)

• Extract explicit claims
• Identify clear logical relationships
• Surface obvious implicit assumptions
• Flag apparent contradictions

Level 3: Deep

• Extract all claims (explicit + implicit)
• Map full logical structure
• Identify hidden assumptions
• Analyze argument completeness
• Red-team reasoning
• Suggest strengthening moves

Best Practices

1. Be charitable: Steelman arguments before critique
2. Distinguish: Separate what's claimed from what's implied
3. Be atomic: One claim per line, no compound assertions
4. Track evidence: Note source/​support for each claim
5. Flag uncertainty: Mark inferential leaps
6. Mind the gaps: Identify missing premises explicitly
7. Stay neutral: Describe structure before evaluating strength

Common Patterns

Argument Chains

Premise 1 (factual) → Premise 2 (causal) → Conclusion (normative)

Implicit Assumptions

Often found by asking: "What must be true for this conclusion to follow?"

Contradictions

Watch for:

• Same speaker, different times
• Different speakers, same topic
• Explicit vs implicit claims

Weak Links

• Unsupported factual claims
• Causal claims without mechanism
• Normative leaps (is → ought)
• Definitional ambiguity

Examples

See references/​examples.md for detailed worked examples.