---
id: wiki-2026-0508-hypostatic-abstraction
title: Hypostatic Abstraction
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Hypostasis, Reification, Subjectal Abstraction]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [logic, semiotics, philosophy, peirce, abstraction]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: en
  framework: peircean-logic
---

# Hypostatic Abstraction

## 매 한 줄
> **"매 predicate 의 subject 으로 transformation — 'X is honest' → 'X has honesty'"**. 매 Peirce (1903) 의 logical operation — 매 first-order property 의 second-order entity 의 conversion. 매 2026 의 ontology engineering, semantic web RDF, type theory 의 reification, LLM 의 conceptual blending 의 modern instances.

## 매 핵심

### 매 정의 (Peirce)
- **Operation**: 매 "p(x)" → "x has property P" — 매 P 의 noun-form entity.
- **예**: 
  - "honey is sweet" → "honey has sweetness"
  - "the function returns int" → "the function has return-type int"
  - "atom is heavy" → "atom has mass"

### 매 distinction from related
- **vs. prescissive abstraction**: 매 prescission = attention 의 isolation (color from shape) — 매 hypostasis = entity 의 creation.
- **vs. reification fallacy**: 매 hypostasis = legitimate logical move; reification fallacy = mistakenly treating abstraction as concrete causal agent.
- **vs. nominalization (linguistics)**: 매 grammatical analog — "destroy" → "destruction".

### 매 utility
- **Reasoning vehicle**: 매 abstract entity 의 quantification 가능 ("there exists a virtue that...").
- **Theory building**: 매 mass, energy, information 의 hypostatic origin.
- **Ontology**: 매 OWL class 의 RDF resource 화.

### 매 응용
1. Math: "function f returns int" → "f has signature ℤ→ℤ".
2. Physics: "object is hot" → "object has temperature T".
3. Law: "act is criminal" → "act has criminality" (mens rea 분석).
4. Programming: type inference 의 reification.
5. Knowledge graph: predicate → resource.

## 💻 패턴

### Predicate to RDF resource (hypostatize)
```python
from rdflib import Graph, URIRef, Literal, RDF
EX = "http://ex.org/"
g = Graph()

# "Alice is honest" → "Alice has honesty(value=true)"
g.add((URIRef(EX + "Alice"), URIRef(EX + "hasVirtue"), URIRef(EX + "Honesty")))
g.add((URIRef(EX + "Honesty"), RDF.type, URIRef(EX + "Virtue")))
```

### Type-level reification (TypeScript)
```typescript
// Before: "f returns number"
function f(x: number): number { return x * 2; }

// Hypostatized: extract the type
type SignatureOf<F> = F extends (...args: infer A) => infer R ? { args: A; ret: R } : never;
type FSig = SignatureOf<typeof f>;  // { args: [number]; ret: number }
```

### Predicate → entity (Peircean diagram)
```python
from dataclasses import dataclass

@dataclass
class HypostaticAbstraction:
    original_predicate: str        # "X is red"
    subject_var: str               # "X"
    abstracted_entity: str         # "redness"
    relation: str                  # "has"

    def express(self, subject: str) -> tuple[str, str]:
        return (
            f"{subject} {self.original_predicate.split(' is ')[1]}",   # original
            f"{subject} {self.relation} {self.abstracted_entity}",    # hypostatized
        )

h = HypostaticAbstraction("X is red", "X", "redness", "has")
print(h.express("the apple"))  # ("the apple red", "the apple has redness")
```

### Detect reification fallacy
```python
def reification_check(claim: str, entity: str) -> bool:
    """Flag if abstract entity assigned causal agency."""
    causal_verbs = {"caused", "did", "decided", "wanted", "forced"}
    return any(f"{entity} {v}" in claim.lower() for v in causal_verbs)

reification_check("Inflation caused the recession", "inflation")  # True (suspect)
```

### Knowledge graph property reification
```turtle
# Direct edge: <Alice> <employs> <Bob>
# Reified (allow metadata on the edge):
:edge1 a rdf:Statement ;
       rdf:subject :Alice ;
       rdf:predicate :employs ;
       rdf:object :Bob ;
       :startDate "2024-01-15" ;
       :salary 90000 .
```

### LLM hypostatization assistant
```python
from anthropic import Anthropic
client = Anthropic()

def hypostatize(claim: str) -> str:
    return client.messages.create(
        model="claude-opus-4-7",
        max_tokens=500,
        system=("Apply Peircean hypostatic abstraction: convert the predicate "
                "into a subject-form entity. Then list questions you can now "
                "ask of that entity."),
        messages=[{"role": "user", "content": claim}],
    ).content[0].text
```

## 매 결정 기준
| 상황 | When to hypostatize |
|---|---|
| Theory building, want to quantify property | yes |
| Need ontology / KG class | yes |
| Reasoning about types, signatures | yes |
| Granting causal agency to abstraction | NO (reification fallacy) |
| Eliminate redundancy in logic | yes (factor predicate) |

**기본값**: 매 hypostatize 의 explicit + reversible. 매 abstract entity 의 causal agent 화 X.

## 🔗 Graph
- 변형: [[Reification]]
- 응용: [[Ontology Engineering]] · [[Type Theory]]
- Adjacent: [[Conceptual Blending]] · [[Knowledge Graph]]

## 🤖 LLM 활용
**언제**: 매 ontology class 의 candidate 의 surface, 매 vague claim 의 quantifiable property 의 reformulation, 매 nominalization 의 unwind.
**언제 X**: 매 reification fallacy 의 detection 의 final arbiter — 매 domain context 의 human review.

## ❌ 안티패턴
- **Reification fallacy**: 매 abstract entity 의 causal agent 의 treatment ("inflation decided to rise"). 매 actual mechanism 의 obscure.
- **Hypostatic explosion**: 매 every adjective 의 entity 화 — 매 ontology bloat.
- **Lost reversibility**: 매 hypostatized form 만 의 retain — 매 original predicate 의 access 어려움.
- **Confusing with prescission**: 매 attention isolation 의 entity creation 의 동일시.

## 🧪 검증 / 중복
- Verified (Peirce CP 4.235, 5.534; Stanford Encyclopedia of Philosophy "Peirce's Logic"; Sowa "Knowledge Representation" 2000).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Peircean operation, RDF/type reification, fallacy distinction 추가 |
