REBEL vs GPT-4 vs SpaCy for Entity Extraction

SpaCy: Fast NER for Standard Types

SpaCy is a production NLP library that provides NER as one component of its processing pipeline. It runs locally, requires no API calls, and processes thousands of documents per second. SpaCy's NER recognizes standard entity types (person, organization, location, date, money) with 87 to 90% F1 using the default models. The transformer-backed model (en_core_web_trf) pushes accuracy to 90% F1 at the cost of slower inference.

Strengths: Extreme throughput (200 to 10,000 docs/sec depending on model). Zero per-document cost. Battle-tested in production since 2015. Easy to fine-tune on custom entity types. Excellent Python API with built-in tokenization, sentence splitting, and POS tagging.

Weaknesses: Fixed entity types unless you fine-tune (requires 200+ labeled examples per type). No relationship extraction. No coreference resolution in the default pipeline. Struggles with entity types that do not look like proper nouns (API endpoints, configuration keys, code references).

Best for: High-volume extraction of standard entity types. The first pass in a tiered extraction pipeline. Applications where latency must be under 10 milliseconds per document.

GPT-4 and Claude: Flexible LLM Extraction

Large language models extract entities and relationships through prompting. You describe what to extract, provide the text, and the LLM returns structured results. GPT-4 and Claude both achieve 85 to 92% F1 on entity extraction tasks and 75 to 85% F1 on relationship extraction, varying by domain complexity and prompt quality.

Strengths: Handles any entity type through prompt description, no training data needed. Extracts relationships alongside entities. Handles coreference, implicit references, and complex sentence structures naturally. Easy to iterate on by changing the prompt. Claude and GPT-4 both produce reliable structured JSON output.

Weaknesses: $0.003 to $0.015 per passage through API calls. 1 to 5 seconds latency per passage. Occasional hallucinated entities, especially with aggressive extraction prompts. Output format can be inconsistent without careful prompt engineering. API dependency means extraction fails when the API is down.

Best for: Domain-specific entity types. Relationship extraction. Exploratory phases where entity types are not yet finalized. Low to medium volume workloads (under 10,000 documents per day). Any situation where flexibility matters more than throughput.

REBEL: Joint Entity and Relationship Extraction

REBEL (Relation Extraction By End-to-end Language generation) is a specialized transformer model from Babelscape that extracts both entities and relationships in a single pass. It frames extraction as a sequence-to-sequence task: input a text passage, output a set of triples. REBEL runs locally like SpaCy but produces triples like an LLM, occupying a unique middle ground in the extraction tool landscape.

Strengths: Extracts entities and typed relationships in a single forward pass. Runs locally with no API costs. Faster than LLM extraction (10 to 50 passages per second on a GPU). Trained on 220 relationship types from Wikidata, covering a broad range of general-purpose relationships. No prompt engineering needed.

Weaknesses: Fixed relationship types from training data. Cannot extract domain-specific relationship types without fine-tuning. Accuracy on domain-specific text (85% on general, 70 to 80% on specialized) is lower than LLM extraction. Requires a GPU for reasonable throughput. Less actively maintained than SpaCy, smaller community and fewer resources for troubleshooting.

Best for: Applications that need both entities and relationships without LLM API costs. General-purpose knowledge graph construction from text that covers common topics (people, organizations, locations, events). Medium-volume workloads where SpaCy's lack of relationship extraction is a limitation but LLM costs are prohibitive.

Head-to-Head Comparison

Practical Recommendations

Start with LLM extraction if you are building a new system and do not yet know your final entity types. The flexibility of prompt-based extraction lets you iterate quickly. Once entity types stabilize, evaluate whether volume and cost justify adding a local model.

Add SpaCy when you process more than 5,000 documents per day and most entities are standard types. Use SpaCy as the fast first pass and the LLM for domain-specific entities only, reducing LLM costs by 60 to 80%.

Consider REBEL when you need relationship extraction at scale without API costs and your relationship types overlap with REBEL's 220 pre-trained types. Check the type list before committing, because fine-tuning REBEL on custom relationship types requires significant effort.

Use a tiered pipeline for production systems: SpaCy for standard entities, REBEL or an LLM for relationships, and an LLM for domain-specific entities that neither SpaCy nor REBEL covers. This maximizes quality while minimizing cost.

Adaptive Recall implements a tiered extraction pipeline that selects the right extraction approach based on the content being stored. Standard entities are identified at high speed, domain-specific entities and relationships are extracted with LLM-level accuracy, and the results are merged into a unified knowledge graph. You get the benefits of all three approaches without choosing between them.