Triple

T4325246
Position Surface form Disambiguated ID Type / Status
Subject Armin Ronacher E96620 entity
Predicate knownFor P22 FINISHED
Object Werkzeug
Werkzeug is a widely used Python WSGI utility library that provides the low-level building blocks for web application frameworks such as Flask.
 E431936 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: Werkzeug | Statement: [Armin Ronacher, knownFor, Werkzeug]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Werkzeug
Context triple: [Armin Ronacher, knownFor, Werkzeug]
  • A. Uvicorn
    Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
  • B. Flask
    Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
  • C. Flask
    Flask is a minor but tough and pugnacious third mate aboard the whaling ship Pequod in Herman Melville’s novel "Moby-Dick."
  • D. Rubinius
    Rubinius is an alternative Ruby implementation featuring a virtual machine and just-in-time compilation, designed for high performance and concurrency.
  • E. The Rack
    The Rack is a 1956 courtroom drama film about the psychological and moral aftermath of a Korean War veteran’s imprisonment and alleged collaboration with the enemy.
  • 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: Werkzeug
Triple: [Armin Ronacher, knownFor, Werkzeug]
Generated description
Werkzeug is a widely used Python WSGI utility library that provides the low-level building blocks for web application frameworks such as Flask.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Werkzeug
Target entity description: Werkzeug is a widely used Python WSGI utility library that provides the low-level building blocks for web application frameworks such as Flask.
  • A. Uvicorn
    Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
  • 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. The Rack
    The Rack is a 1956 courtroom drama film about the psychological and moral aftermath of a Korean War veteran’s imprisonment and alleged collaboration with the enemy.
  • 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_69b34542fd908190b11b08faad8decfd completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3512ec18481908a7b5c29b3902b53 completed March 12, 2026, 11:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d094f5fc819083eddc234f46f6a1 completed March 14, 2026, 9:18 p.m.
NEDg Description generation batch_69b5d10a20248190b3214509cb637ff4 completed March 14, 2026, 9:20 p.m.
NED2 Entity disambiguation (via description) batch_69b5d4f08ac4819097e71276be11403d completed March 14, 2026, 9:36 p.m.
Created at: March 12, 2026, 11:13 p.m.