atters because it forces a paradigm shift: evaluation must move from query-layer proxies to construction-layer auditing. It enables teams to detect silent data loss before it propagates into downstream analytics, compliance reporting, or automated decision systems.

Core Solution

The solution requires decoupling format detection from constraint application, implementing surface-form anchoring mitigation, and replacing retrieval proxies with direct graph validation. The architecture follows a three-stage pipeline: format-aware normalization, constraint-gated extraction, and fidelity auditing.

Architecture Decisions

1. Format-Aware Normalization: Wide matrices must be flattened into long-form records before schema application. This breaks column-name anchoring by converting temporal headers into explicit attribute-value pairs.
2. Constraint Gating: Schemas should not be applied unconditionally. A compatibility layer evaluates format-schema alignment and dynamically relaxes or tightens constraints based on detected layout.
3. Direct Fidelity Auditing: Retrieval scores are insufficient. The pipeline must compare extracted triples against structural heuristics or gold standard references, measuring entity inflation, refusal rates, and coverage gaps directly on the graph.

Implementation (TypeScript)

The following implementation demonstrates a decoupled extraction pipeline. It replaces monolithic extraction functions with composable stages, introduces format detection, and implements a fidelity auditor that measures construction quality without relying on retrieval proxies.

// interfaces.ts
export interface TabularRecord {
  rowId: string;
  attributes: Record<string, string | number>;
  formatType: 'wide' | 'long' | 'normalized';
}

export interface ExtractionConstraint {
  entityType: string;
  requiredFields: string[];
  strictness: 'loose' | 'moderate' | 'strict';
}

export interface GraphTriple {
  subject: string;
  predicate: string;
  object: string;
  confidence: number;
}

export interface FidelityReport {
  factCoverage: number;
  entityInflationRate: number;
  extractionRefusalRate: number;
  directGraphGap: number;
  formatSchemaAlignment: 'aligned' | 'misaligned' | 'coupled';
}

// formatDetector.ts
export class FormatDetector {
  static analyze(rawRows: Record<string, string | number>[]): TabularRecord[] {
    const columnCount = Object.keys(rawRows[0] || {}).length;
    const isWide = columnCount > 12 && /year|date|period/i.test(Object.keys(rawRows[0] || {}).join(','));
    
    return rawRows.map((row, idx) => ({
      rowId: `row_${idx}`,
      attributes: row,
      formatType: isWide ? 'wide' : 'long'
    }));
  }
}

// constraintGater.ts
export class ConstraintGater {
  static evaluateAlignment(
    records: TabularRecord[],
    constraint: ExtractionConstraint
  ): 'aligned' | 'misaligned' | 'coupled' {
    const wideCount = records.filter(r => r.formatType === 'wide').length;
    const ratio = wideCount / records.length;
    
    if (constraint.strictness === 'strict' && ratio > 0.6) return 'coupled';
    if (constraint.strictness === 'loose' && ratio > 0.8) return 'misaligned';
    return 'aligned';
  }
}

// extractionPipeline.ts
export class TabularGraphExtractor {
  constructor(
    private llmClient: any,
    private constraint: ExtractionConstraint
  ) {}

  async extract(records: TabularRecord[]): Promise<{ triples: GraphTriple[]; report: FidelityReport }> {
    const alignment = ConstraintGater.evaluateAlignment(records, this.constraint);
    const adjustedConstraint = this.adjustConstraint(alignment);
    
    const triples: GraphTriple[] = [];
    let refused = 0;
    let inflated = 0;
    
    for (const record of records) {
      const normalized = this.normalizeWideFormat(record);
      const result = await this.llmClient.extract({
        context: normalized.attributes,
        schema: adjustedConstraint
      });
      
      if (result.status === 'refused') {
        refused++;
        continue;
      }
      
      const extracted = result.triples.map(t => ({
        subject: t.subject,
        predicate: t.predicate,
        object: t.object,
        confidence: t.confidence || 0.85
      }));
      
      inflated += this.detectInflation(extracted, record);
      triples.push(...extracted);
    }
    
    const total = records.length;
    const report: FidelityReport = {
      factCoverage: ((total - refused) / total) * 100,
      entityInflationRate: (inflated / triples.length) * 100,
      extractionRefusalRate: (refused / total) * 100,
      directGraphGap: this.calculateGraphGap(triples, adjustedConstraint),
      formatSchemaAlignment: alignment
    };
    
    return { triples, report };
  }
  
  private normalizeWideFormat(record: TabularRecord): TabularRecord {
    if (record.formatType !== 'wide') return record;
    
    const flat: Record<string, string | number> = {};
    for (const [key, value] of Object.entries(record.attributes)) {
      if (/year|date|period/i.test(key)) {
        flat[`temporal_${key}`] = value;
      } else {
        flat[key] = value;
      }
    }
    
    return { ...record, attributes: flat, formatType: 'normalized' };
  }
  
  private adjustConstraint(alignment: string): ExtractionConstraint {
    if (alignment === 'coupled') {
      return { ...this.constraint, strictness: 'moderate' };
    }
    return this.constraint;
  }
  
  private detectInflation(triples: GraphTriple[], record: TabularRecord): number {
    const subjects = new Set(triples.map(t => t.subject));
    const expected = Object.keys(record.attributes).length;
    return Math.max(0, subjects.size - expected);
  }
  
  private calculateGraphGap(triples: GraphTriple[], constraint: ExtractionConstraint): number {
    const required = constraint.requiredFields.length;
    const covered = new Set(triples.map(t => t.predicate)).size;
    return Math.max(0, ((required - covered) / required) * 100);
  }
}

Why This Architecture Works

• Format normalization breaks surface-form anchoring: By converting wide headers into explicit temporal attributes, the LLM stops treating column names as entity boundaries. This directly addresses the token ablation findings.
• Constraint gating prevents super-additive degradation: The pipeline detects format-schema misalignment and dynamically relaxes strictness. This avoids the catastrophic coverage drops observed when rigid schemas meet wide matrices.
• Direct graph validation replaces retrieval proxies: The FidelityReport measures construction quality at the triple level. Entity inflation, refusal rates, and graph gaps are calculated without relying on vector search or hybrid retrieval, which mask structural deficiencies.

Pitfall Guide

1. The Schema Neutrality Fallacy

Explanation: Assuming extraction schemas operate independently of source format. In reality, strict schemas amplify format-induced anchoring, causing super-additive degradation. Fix: Implement a compatibility layer that evaluates format-schema alignment before extraction. Dynamically adjust constraint strictness based on detected layout.

2. Wide-Matrix Blindness

Explanation: Treating all CSVs as structurally equivalent. Wide matrices with temporal columns trigger surface-form anchoring, which long-form data avoids. Fix: Run format detection on ingestion. Flatten wide matrices into long-form records before applying any extraction constraints.

3. Retrieval Metric Deception

Explanation: Relying on vector search scores or top-k retrieval accuracy to validate graph quality. Retrieval masks construction gaps with ≤1pp delta while direct access reveals +47.6pp gaps. Fix: Replace retrieval proxies with direct graph auditing. Measure triple coverage, entity inflation, and structural completeness at the construction layer.

4. Entity Inflation Cascade

Explanation: Rigid schemas force LLMs to invent entities to satisfy type constraints, especially when column names are misinterpreted as categorical values. Fix: Implement entity deduplication and cardinality checks. Flag schemas that produce subject counts exceeding attribute counts by >15%.

5. Extraction Refusal Triggers

Explanation: Mismatched format-schema pairs cause LLMs to refuse extraction rather than approximate. This drops fact coverage below unconstrained baselines. Fix: Add fallback extraction modes with relaxed constraints. Log refusal reasons and trigger automatic schema adjustment on repeated failures.

6. Surface-Form Anchoring Overreliance

Explanation: LLMs anchor extraction to exact column strings. When headers contain dates, units, or abbreviations, the model treats them as entity names. Fix: Preprocess headers into semantic descriptors. Use prompt templates that explicitly separate temporal axes from entity attributes.

7. Cross-LLM Assumption

Explanation: Assuming all model families exhibit identical coupling behavior. One major family shows only partial activation due to architectural differences in tabular tokenization. Fix: Benchmark extraction pipelines across multiple LLM families. Do not port constraint configurations without format-specific validation.

Production Bundle

Action Checklist

• Audit source CSVs for wide-format layouts before schema application
• Implement format detection and automatic normalization to long-form records
• Decouple constraint application from extraction logic using a gating layer
• Replace retrieval-based evaluation with direct graph fidelity auditing
• Monitor entity inflation and extraction refusal rates in production logs
• Benchmark pipeline against CSVFidelity-Bench (15 datasets, 1,892 gold facts)
• Configure fallback extraction modes for misaligned format-schema pairs
• Validate constraint behavior across multiple LLM families before deployment

Decision Matrix

Scenario	Recommended Approach	Why	Cost Impact
Wide temporal CSV + strict schema	Normalize format, relax constraint to moderate	Prevents super-additive coupling and entity inflation	Low (preprocessing overhead)
Long-form CSV + strict schema	Apply schema directly	Format alignment minimizes anchoring risk	None
Retrieval-heavy GraphRAG	Add direct graph validation layer	Retrieval masks construction gaps up to +47.6pp	Medium (audit pipeline)
Multi-LLM deployment	Format-specific constraint tuning	One family shows partial coupling activation	Low (configuration matrix)
Compliance-critical analytics	Use CSVFidelity-Bench for validation	Gold standard facts expose silent data loss	Medium (benchmark integration)

Configuration Template

# extraction-pipeline.config.yaml
format_detection:
  wide_threshold: 12
  temporal_patterns: ["year", "date", "period", "q[1-4]"]
  normalization: true

constraint_gating:
  strictness_map:
    aligned: "strict"
    misaligned: "moderate"
    coupled: "loose"
  inflation_threshold: 0.15
  refusal_fallback: true

fidelity_audit:
  metrics:
    - fact_coverage
    - entity_inflation_rate
    - extraction_refusal_rate
    - direct_graph_gap
  retrieval_proxy: false
  benchmark: "CSVFidelity-Bench"
  gold_fact_count: 1892
  domains: ["economics", "health", "education", "demographics", "trade", "energy"]

llm_routing:
  families: ["family_a", "family_b", "family_c"]
  coupling_detection: true
  partial_activation_handling: "relax_constraints"

Quick Start Guide

1. Ingest and Detect: Load your CSV into the pipeline. Run FormatDetector.analyze() to classify layout as wide or long.
2. Normalize and Gate: Flatten wide matrices into long-form records. Pass through ConstraintGater.evaluateAlignment() to determine format-schema compatibility.
3. Extract and Audit: Run TabularGraphExtractor.extract(). The pipeline applies adjusted constraints, collects triples, and generates a FidelityReport with direct graph metrics.
4. Validate and Iterate: Compare extraction output against CSVFidelity-Bench gold facts. If direct graph gap exceeds 10%, adjust constraint strictness or refine header normalization.
5. Deploy with Monitoring: Route production traffic through the gated pipeline. Log refusal rates and inflation metrics. Trigger automatic schema relaxation when coupling is detected.