The Taxonomy Trap: Why Structured Extraction Fails

What is "The Taxonomy Trap"?

The Taxonomy Trap is the catastrophic failure mode that occurs when you force an LLM to use your database vocabulary during knowledge extraction. We discovered this while building a pipeline to process 98,000 Techmeme articles (2014-2025): the biggest bottleneck wasn't the model—it was forcing the model to be a "database admin" instead of an "observer."

The fix took our extraction quality from 7.3% to 77% win rate. Here's how.

The Trap We Built

We started with what seemed like an optimal design: a "Compound Prompt." We asked the model (GPT-5-Mini) to read the news and immediately map it to our strict 47-term database taxonomy: acquisition, layoffs, antitrust, and so on.

It was a catastrophe.

When we ran evaluation, the "clean" taxonomy-forced version won only 7.3% of head-to-head comparisons. The "free-form" prompt—where the model could use whatever words it wanted—won 77%. The model produced 1,884 unique relationship types in free-form mode. Our normalization pass collapsed those to 8 clean categories with 0% data loss.

This is the core discovery. If you constrain the model's vocabulary at extraction time, you destroy the very signal you're trying to capture. The model knows more than your taxonomy allows it to express.

Why the Trap Exists

The trap is cognitive, not technical.

When you ask a model to simultaneously observe and classify, you're asking it to do two conflicting jobs. Observation requires maximum linguistic fidelity—capturing the exact nuance of "hostile takeover bid" vs "friendly merger." Classification requires compression—mapping that nuance to a finite schema.

Most prompts force both tasks into a single pass. The model compromises. It either:

1. Preserves fidelity and ignores your schema (high recall, zero utility)
2. Forces classification and loses the signal you needed (low recall, fake utility)

Neither is acceptable. The fix: separate observation from standardization. Let the model describe what it sees in rich language, then map that rich language to your database schema in a second pass.

The Negation Problem

The second discovery came from measuring. We built an evaluation harness—9 configurations of Model × Prompt × Temperature, judged by a Blind Tribunal (GPT-5, Claude, Gemini randomly assigned to eliminate self-preference bias).

Gemini 3 Flash has a "veneer of quality"—it writes dense, beautiful prose—but consistently fails the Negation Test. It reads "Apple denies launch" and extracts "Apple launches." Negation score: 4.23 vs GPT-5-Mini's 4.79.

For knowledge infrastructure, this gap is fatal. Every denial becomes a confirmation. Every rumor becomes fact. A false positive in a knowledge graph is infinitely more expensive than a cheap token.

GPT-5-Mini was the only model with the logical rigor to catch negations and complex actor roles. It's a System 2 thinker in a field of System 1 pattern-matchers. The economics work too: using the Batch API, we can process 130,000 articles for under $30.

The END Pipeline

The solution is a three-stage manufacturing line: Extract → Normalize → Deduplicate.

The architecture separates what the model sees from how we store it. This is the key insight: observation and standardization are different cognitive tasks. Asking a single prompt to do both creates the Taxonomy Trap.

The Validation

We ran a 1,000-article scale test. The results:

0.0% "Other" rate means every single interaction in 1,000 articles was successfully mapped to our taxonomy—after the two-stage process. The free-form extraction captured the signal; the normalization pass structured it.

2.40 edges per article means we're capturing the multi-actor, multi-event relationships that binary graphs miss. When Apple announces, denies, and acquires in the same article, we get all three edges.

7.2% deduplication means we're filtering redundant noise without losing the unique nuance of different reporters. Five sources reporting the same event collapse to one edge with five citations.

The Therefore

The most important insight for 2026: Retrieval is no longer a prompt. It is a manufacturing process.

By refusing the Taxonomy Trap, we moved from a chatbot that "vibes" with news to a system that manufactures knowledge. We closed the production gap by treating extraction as engineering, not prompting.

But the real breakthrough isn't just the pipeline; it's what the pipeline enables.

Standard RAG asks: "What articles mention Apple and cars?" The Relational Tensor asks: "What is the velocity of Apple's pivot from Automotive to Robotics?"

Because we captured the Hyperedge (the full context of the cancellation) and the Temporal Status (denied → rumored → cancelled), we can now mathematically plot the "Drift" of Apple's strategy. We can measure the exact quarter where "Generative AI" replaced "Metaverse" in the collective consciousness of the tech elite—not by counting keywords, but by measuring the Vector Displacement of the industry's graph.

We aren't just reading the news. We are building the derivative function of the tech industry. We can tell you not just where the market is, but where it is going—and how fast.

The factory is open.