objectivist-ner

Most Named Entity Recognition tools treat language as a bag of words to be statistically tagged.

This tool takes a different approach.

It is built on the Objectivist recognition that concepts are not arbitrary labels — they are integrations of observed reality, formed by identifying essential characteristics and omitting measurements. A valid concept must be grounded in percepts, organized hierarchically, and maintain identity across contexts.

That is why objectivist-ner emphasizes:

• Exact entity spans — because a concept must refer to something specific in reality
• Hierarchical classification — because proper concept formation requires understanding genus and differentia
• Negation detection — because the relationship of a concept to existence is epistemologically essential
• Coreference resolution — because the law of identity demands we recognize the same existent across multiple descriptions
• Relations — because concepts do not exist in isolation, they integrate into propositions

It runs completely locally using a small language model. No API keys. No data leaves your machine.

What Makes This Different

1. Assertion vs Negation vs Hypothetical

"The patient has diabetes but does not have cancer. He might develop hypertension."

Typical NER sees three diseases. objectivist-ner sees three different relationships to reality:

ner --detect-negation "The patient has diabetes but does not have cancer. He might develop hypertension."

[
  { "class": "disease", "text": "diabetes", "assertion": "present" },
  { "class": "disease", "text": "cancer", "assertion": "negated" },
  { "class": "disease", "text": "hypertension", "assertion": "hypothetical" }
]

The assertion field tells you whether the text claims something is present, negated, or hypothetical.

2. Identity Across References

"Dr. Chen published a paper. She later won the Nobel Prize. The neurologist was celebrated."

Typical NER sees three separate people. objectivist-ner knows they are the same person:

ner --resolve "Dr. Chen published a paper. She later won the Nobel Prize. The neurologist was celebrated."

[
  {
    "class": "person",
    "text": "Dr. Chen",
    "entity_id": "e1",
    "is_canonical": true
  },
  {
    "class": "person",
    "text": "She",
    "entity_id": "e1",
    "is_canonical": false
  },
  {
    "class": "person",
    "text": "The neurologist",
    "entity_id": "e1",
    "is_canonical": false
  },
  {
    "class": "event",
    "text": "the Nobel Prize",
    "entity_id": "e2",
    "is_canonical": true
  }
]

entity_id groups coreferent mentions. is_canonical marks the most specific reference.

3. Hierarchical Classification

Define your ontology as a tree with mixed arrays (leaf nodes) and objects (nested hierarchies):

organism
├── person
└── animal
    ├── dog
    └── cat

idea
├── dream
└── principle

ner --taxonomy '{"organism":["person",{"animal":["dog","cat"]}],"idea":["dream","principle"]}' \
  "The child recounted a vivid dream about the golden retriever."

[
  {
    "class": "person",
    "text": "The child",
    "taxonomyPath": ["organism", "person"]
  },
  {
    "class": "dream",
    "text": "a vivid dream",
    "taxonomyPath": ["idea", "dream"]
  },
  {
    "class": "dog",
    "text": "the golden retriever",
    "taxonomyPath": ["organism", "animal", "dog"]
  }
]

The model classifies at the most specific (leaf) level, and taxonomyPath preserves the full hierarchy.

4. Conceptual Integration (Relations)

ner --relations "Dr. Chen works at MIT and collaborates with Prof. Wright"

{
  "entities": [
    { "class": "person", "text": "Dr. Chen" },
    { "class": "organization", "text": "MIT" },
    { "class": "person", "text": "Prof. Wright" }
  ],
  "relations": [
    { "source": "Dr. Chen", "target": "MIT", "relation": "works at" },
    {
      "source": "Dr. Chen",
      "target": "Prof. Wright",
      "relation": "collaborates with"
    }
  ]
}

Relations show how entities connect — extracting the "connective tissue" between concepts.

You can also categorize relations by class:

ner --relations --relation-classes "employment,location,causal,professional" \
  "Dr. Chen works at MIT and collaborates with Prof. Wright"

{
  "entities": [
    { "class": "person", "text": "Dr. Chen" },
    { "class": "organization", "text": "MIT" },
    { "class": "person", "text": "Prof. Wright" }
  ],
  "relations": [
    {
      "source": "Dr. Chen",
      "target": "MIT",
      "relation": "works at",
      "class": "employment"
    },
    {
      "source": "Dr. Chen",
      "target": "Prof. Wright",
      "relation": "collaborates with",
      "class": "professional"
    }
  ]
}

The class field categorizes the relation type (e.g., employment, causal, spatial), allowing you to group and analyze connections by category.

Installation

bun install -g objectivist-ner

Quick Start

# Basic extraction
ner "the cat is blue and is feeling sad"

# Choose quality vs speed
ner --fast "simple text"
er --balanced "moderate text"
er --best "complex text"

Usage Examples

Constrain entity classes

ner "John works at Google" --classes person,organization

[
  { "class": "person", "text": "John" },
  { "class": "organization", "text": "Google" }
]

Constrain attribute keys

ner "Alice is sad in Paris" --attributes emotional_state,location

[
  {
    "class": "person",
    "text": "Alice",
    "attributes": { "emotional_state": "sad", "location": "Paris" }
  }
]

Constrain attribute values

ner "The sky is blue" --attr-values '{"color":["blue","red","green"]}'

[
  {
    "class": "object",
    "text": "sky",
    "attributes": { "color": "blue" }
  }
]

Schema files

Define your ontology once and reuse it:

{
  "taxonomy": {
    "organism": ["person", "animal"],
    "animal": ["dog", "cat"]
  },
  "attributes": ["role", "location"],
  "relations": ["works_at", "collaborates_with"]
}

ner --schema ontology.json "Dr. Chen works at MIT"

File and batch processing

# Process a long document (auto-chunked)
ner --file document.txt

# Process a JSONL file
ner --batch inputs.jsonl

# Process a directory of .txt files
ner --batch ./documents/

Read from stdin

echo "the cat is blue" | ner
cat article.txt | ner --detect-negation

Model Tiers

| Flag | Size | Download | Best for | | ------------ | ------ | -------- | ------------------------------- | | --fast | Small | ~0.9 GB | Simple text, single entities | | --balanced | Medium | ~2.3 GB | Moderate complexity, most tasks | | --best | Large | ~4.5 GB | Dense text, rare entity types |

--best is the default. See Benchmarks.

Options Reference

| Flag | Description | | --------------------------------- | -------------------------------------------------- | | --fast | Use smallest model | | --balanced | Use mid-size model | | --best | Use largest model (default) | | -c, --classes <list> | Allowed entity classes | | -a, --attributes <list> | Allowed attribute keys | | --attr-values <json> | Enum map for attribute values | | --taxonomy <json> | Class hierarchy (parent → children) | | --relations | Extract relations between entities | | --relation-classes <list> | Allowed relation classes (e.g. employment,causal) | | --resolve | Resolve coreferences (adds entity_id) | | --detect-negation | Add assertion field (present/negated/hypothetical) | | --include-confidence | Add confidence field (low/medium/high) | | --schema <path> | Load schema from JSON file | | --file <path> | Read from file (with chunking) | | --batch <path> | Process JSONL file or directory | | --system-prompt <string> | Replace system prompt | | --system-prompt-append <string> | Append to system prompt | | --compact | Compact JSON output | | -m, --model <uri> | Use custom GGUF model |

Benchmarks

Tested on a complex input with 11 entities across 6 classes:

| Entity | --fast | --balanced | --best | | ------------------ | -------- | ------------ | --------- | | Dr. Maria Chen | person | person | person | | Prof. James Wright | person | person | person | | MGH | — | org | org | | Oxford University | — | org | org | | BRCA3-delta | — | disease | disease | | Bangladesh | — | — | location | | Pfizer | — | org | org | | Nexavion | — | drug | drug | | WHO | — | — | org | | Geneva summit | — | event | location | | Boston | location | — | location | | Found | 3/11 | 8/11 | 11/11 |

Epistemological Design

Each feature maps to an Objectivist principle:

• --resolve — The law of identity (A is A)
• --taxonomy — Hierarchical concept formation (genus and differentia)
• --detect-negation — Grounding concepts in reality (existence vs non-existence)
• --relations — Conceptual integration (concepts form connected propositions)
• Grammar enforcement — Non-contradiction (structure prevents invalid values)

Custom Models

Use any GGUF model:

ner "text" --model "hf:unsloth/Qwen3-8B-GGUF:Qwen3-8B-Q4_K_M.gguf"
ner "text" --model ./my-model.gguf

Complete Ontology Schema Example

Schema files let you define your entire ontology in one place:

{
  "taxonomy": {
    "organism": [
      "person",
      {
        "animal": ["dog", "cat", "bird"]
      }
    ],
    "place": [
      "city",
      "country",
      {
        "building": ["hospital", "school", "office"]
      }
    ],
    "institution": ["company", "university", "government_agency"],
    "concept": ["idea", "theory", "principle"]
  },
  "classes": ["person", "organization", "location", "event", "disease", "drug"],
  "attributes": ["role", "age", "location", "date", "affiliation", "specialty"],
  "attrValues": {
    "role": ["doctor", "researcher", "patient", "student"],
    "location": ["Boston", "New York", "London", "Tokyo"]
  },
  "relations": [
    "works_at",
    "located_in",
    "treats",
    "studies",
    "collaborates_with"
  ],
  "relationClasses": [
    "employment",
    "spatial",
    "medical",
    "academic",
    "professional"
  ]
}

Usage:

ner --schema complete-ontology.json "Dr. Chen works at Massachusetts General Hospital in Boston"

This loads taxonomy, classes, attributes, attribute values, relation types, and relation classes all at once.

Building Principles from Percepts

objectivist-ner extracts the raw perceptual material from any text: concrete entities with exact spans, hierarchical taxonomy (genus and differentia), coreferences preserving identity, assertions vs. negations, and relations.

This supplies the inductive base required by Objectivist epistemology.

Feed it philosophical works, scientific papers, historical accounts, or any reality-grounded writing. The parsed output gives you the concretes needed to form concepts and derive principles—anchored in observed facts, not floating abstractions.

Use it to mine source material, reduce arguments to their perceptual roots, and build your own objective knowledge from existence.