Triple

T11216058
Position Surface form Disambiguated ID Type / Status
Subject PEP 484 E265441 entity
Predicate relatedTo P37 FINISHED
Object PEP 526
PEP 526 is a Python Enhancement Proposal that introduced a standard syntax for variable and attribute type annotations in Python.
 E911262 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: PEP 526 | Statement: [PEP 484, relatedTo, PEP 526]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PEP 526
Context triple: [PEP 484, relatedTo, PEP 526]
  • A. PEP 570
    PEP 570 is the Python Enhancement Proposal that introduced positional-only parameters to Python function definitions, formalizing a syntax for arguments that must be passed by position.
  • B. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • C. PEP 578
    PEP 578 is a Python enhancement proposal that introduces a security audit hook framework to help monitor and control runtime events in Python applications.
  • D. PEP 590
    PEP 590 is the Python Enhancement Proposal that introduced the "vectorcall" protocol to speed up and standardize function calls in CPython.
  • E. PEP 626
    PEP 626 is a Python Enhancement Proposal that precisely defines how Python should map executed bytecode instructions to source code lines, improving debugging, coverage measurement, and tooling accuracy.
  • 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: PEP 526
Triple: [PEP 484, relatedTo, PEP 526]
Generated description
PEP 526 is a Python Enhancement Proposal that introduced a standard syntax for variable and attribute type annotations in Python.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PEP 526
Target entity description: PEP 526 is a Python Enhancement Proposal that introduced a standard syntax for variable and attribute type annotations in Python.
  • A. PEP 570
    PEP 570 is the Python Enhancement Proposal that introduced positional-only parameters to Python function definitions, formalizing a syntax for arguments that must be passed by position.
  • B. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • C. PEP 578
    PEP 578 is a Python enhancement proposal that introduces a security audit hook framework to help monitor and control runtime events in Python applications.
  • D. PEP 590
    PEP 590 is the Python Enhancement Proposal that introduced the "vectorcall" protocol to speed up and standardize function calls in CPython.
  • E. PEP 626
    PEP 626 is a Python Enhancement Proposal that precisely defines how Python should map executed bytecode instructions to source code lines, improving debugging, coverage measurement, and tooling accuracy.
  • 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_69d6aac59460819089b9848b27f57848 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8e8eef48190932a85784ce15c86 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69e49762e3188190ba3c0e01cf04f6a1 completed April 19, 2026, 8:50 a.m.
NEDg Description generation batch_69e49d37989881909c7e75ddfff06726 completed April 19, 2026, 9:15 a.m.
NED2 Entity disambiguation (via description) batch_69e49f41a1f8819087cc15527dc7ff63 completed April 19, 2026, 9:24 a.m.
Created at: April 8, 2026, 9:30 p.m.