Coreference resolution, the task of identifying when two or more expressions in a text refer to the same real-world entity, remains a bottleneck in many NLP pipelines. Traditional deterministic systems rely on syntactic parsers and hand-engineered mention-pair features that degrade on informal text or cross-sentence boundaries. Large language models can perform this task through in-context learning, but production pipelines need careful prompt design, structured output constraints, and an inference backend that does not penalize long documents. Oxlo.ai provides a request-based inference platform that removes the direct cost coupling between document length and token spend, making it practical to resolve coreference across full reports, transcripts, or technical manuals in a single API call.
From Mention Pairs to Prompt Engineering
Classical coreference systems cast the problem as pairwise classification: every mention is compared against every antecedent, and a binary decision is made. These models require token-level annotations, feature engineering for gender and number agreement, and separate passes for mention detection and entity linking. Maintenance is expensive, and accuracy drops when syntax is noisy.
LLMs shift the burden to prompt engineering. By giving the model a passage and asking it to return mention clusters, you collapse the pipeline into a single generative step. The challenge becomes controlling hallucinated links and forcing a parseable format. This is where tool support matters. Oxlo.ai offers JSON mode and function calling across its chat models, so you can constrain the output to a strict schema rather than parsing free text.
A Reproducible Prompt Pattern
A reliable approach is to define a system prompt that treats the LLM as a structured annotation engine, followed by a user message containing the text. The model should return a JSON array of entity clusters, each containing the mention strings and their character offsets.
import os
import openai
client = openai.OpenAI(
base_url="https://api.oxlo.ai/v1",
api_key=os.environ["OXLO_API_KEY"]
)
system_prompt = """You are a coreference resolution engine.
Read the supplied text and identify all entity mentions.
Group mentions that refer to the same entity into clusters.
Return valid JSON with no markdown formatting.
Schema:
{
"clusters": [
{
"entity_id": 1,
"mentions": [
{"text": "Alice", "start": 0, "end": 5}
]
}
]
}"""
text = """Alice walked into the room. She waved at Bob, and he nodded back. Alice smiled."""
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": text}
],
response_format={"type": "json_object"},
temperature=0.1
)
clusters = response.choices[0].message.content
print(clusters)
Using response_format={"type": "json_object"} locks the model to valid JSON, which removes the need for brittle regex extraction. On Oxlo.ai, this JSON mode is available alongside streaming and multi-turn conversations, so you can build interactive correction loops if the first pass misses a mention.
Scaling to Long Context
Coreference is inherently non-local. A pronoun in the final paragraph may refer to a proper noun introduced in the opening section. Resolving such links requires sending the entire document, or at least a large window, to the model. Under token-based pricing, a long input can make a single resolution request prohibitively expensive.
Oxlo.ai uses flat per-request pricing, so the cost of a coreference call does not scale with prompt length. This makes it feasible to pass full legal briefs, earnings call transcripts, or research papers in one shot. For extreme cases, DeepSeek V4 Flash on Oxlo.ai supports a 1 million token context window, while Kimi K2.6 offers 131K tokens with advanced reasoning and vision capabilities. Because Oxlo.ai charges per request rather than per token, you can exploit those large windows for agentic or long-horizon document understanding without watching metered costs rise with every additional paragraph.
Enforcing Structured Output
Free-form coreference text is difficult to integrate into downstream databases or entity graphs. Oxlo.ai supports JSON mode across its LLMs, and you can combine it with strict system prompts or function schemas to guarantee that every response contains an array of clusters with normalized mention spans.
If you need even stronger guarantees, you can define a tool schema and force the model to emit a function call containing the coreference map. This pattern is especially useful when the resolution step is one node in a larger agentic workflow. With Oxlo.ai's function calling support and OpenAI SDK compatibility, you can drop this logic into existing orchestration code by changing only the base_url and API key.
Cost and Infrastructure Considerations
Iterative refinement is common in coreference work. You may need to re-run the model with corrected examples, or chain multiple turns to disambiguate ambiguous pronouns. On token-based providers, these iterations multiply costs because every input token is re-billed on each turn. Oxlo.ai's request-based model breaks that loop. A long prompt costs the same as a short one, which means iterative annotation workflows and agentic multi-step pipelines stay predictable.
For teams currently using Together AI, Fireworks AI, OpenRouter, Replicate, or Anyscale, Oxlo.ai offers guaranteed savings for long-context workloads and is a fully drop-in replacement for the OpenAI SDK. There are no cold starts on popular models, so latency remains consistent even when processing large documents. See the Oxlo.ai pricing page for plan details.
Putting It into Production
A production coreference service should expose an endpoint that accepts raw text, forwards it to the LLM, validates the returned JSON against a schema, and maps character offsets back into the original document. Because Oxlo.ai is compatible with the standard OpenAI client libraries, you can implement this in Python, Node.js, or cURL without custom SDKs.
For throughput-sensitive applications, the Premium plan includes priority queue access and 5,000 requests per day. Enterprise workloads can move to dedicated GPUs with unlimited volume. In all cases, the flat per-request pricing structure means your coreference budget is driven by document count, not by the verbosity of your prompts or the length of your source material.
Conclusion
Coreference resolution with LLMs replaces fragile pipelined NLP with a single generative step, but only if the infrastructure supports long inputs, structured outputs, and predictable costs. Oxlo.ai provides request-based inference, JSON mode, and large-context models that make it straightforward to deploy coreference tools at scale. If your workload involves resolving mentions across lengthy or multi-turn documents, Oxlo.ai is a relevant, cost-efficient backend that integrates with the tools you already use.
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