Triple

T816338
Position Surface form Disambiguated ID Type / Status
Subject FastAPI E17658 entity
Predicate compatibleWith P203 FINISHED
Object Uvicorn
Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
 E97061 NE FINISHED

How this triple was built (4 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Uvicorn | Statement: [FastAPI, compatibleWith, Uvicorn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Uvicorn
Context triple: [FastAPI, compatibleWith, Uvicorn]
  • A. FastAPI
    FastAPI is a modern, high-performance Python framework for building APIs with automatic interactive documentation and type hint–driven validation.
  • B. Flask
    Flask is a minor but tough and pugnacious third mate aboard the whaling ship Pequod in Herman Melville’s novel "Moby-Dick."
  • C. Flask
    Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
  • D. Deno
    Deno is a modern, secure JavaScript and TypeScript runtime created by Ryan Dahl as a successor to Node.js, featuring built-in TypeScript support and a permission-based security model.
  • E. Django
    Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design for building secure, scalable web applications.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Uvicorn
Triple: [FastAPI, compatibleWith, Uvicorn]
Generated description
Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Uvicorn
Target entity description: Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
  • A. FastAPI
    FastAPI is a modern, high-performance Python framework for building APIs with automatic interactive documentation and type hint–driven validation.
  • B. Flask
    Flask is a minor but tough and pugnacious third mate aboard the whaling ship Pequod in Herman Melville’s novel "Moby-Dick."
  • C. Flask
    Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
  • D. Rubinius
    Rubinius is an alternative Ruby implementation featuring a virtual machine and just-in-time compilation, designed for high performance and concurrency.
  • E. Deno
    Deno is a modern, secure JavaScript and TypeScript runtime created by Ryan Dahl as a successor to Node.js, featuring built-in TypeScript support and a permission-based security model.
  • F. None of above. chosen

Provenance (5 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a4937bcaac8190a322524ac6f45a5a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4ab621d2c819083f10bff4f66c482 completed March 1, 2026, 9:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69a76d8d1a448190be8494fa2776615a completed March 3, 2026, 11:23 p.m.
NEDg Description generation batch_69a78bd0a1d48190907434a17853dfb1 completed March 4, 2026, 1:33 a.m.
NED2 Entity disambiguation (via description) batch_69a78c3a57d88190a994ed44bcb2d8d1 completed March 4, 2026, 1:34 a.m.
Created at: March 1, 2026, 7:38 p.m.