Triple
T2313377
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Process PEP |
E51007
|
entity |
| Predicate | hasExample |
P1259
|
FINISHED |
| Object |
PEP 8016
PEP 8016 is a Python Enhancement Proposal that defined a governance model for the Python project following Guido van Rossum’s retirement as BDFL.
|
E255502
|
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 8016 | Statement: [Process PEP, hasExample, PEP 8016]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 8016 Context triple: [Process PEP, hasExample, PEP 8016]
-
A.
PEP 0
PEP 0 is the index document that lists and tracks the status of all Python Enhancement Proposals (PEPs) in the Python community.
-
B.
PEP 1
PEP 1 is the foundational Python Enhancement Proposal that defines the purpose, structure, and workflow for all other PEPs in the Python community process.
-
C.
PEP 13
PEP 13 is the Python Enhancement Proposal that defines the process and rules for selecting and operating the Python Steering Council, the core governance body of the Python project.
-
D.
PEP 634
PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
-
E.
PEPM
PEPM (Partial Evaluation and Program Manipulation) is an ACM SIGPLAN-sponsored symposium focused on research in program analysis, transformation, and generation techniques.
- 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 8016 Triple: [Process PEP, hasExample, PEP 8016]
Generated description
PEP 8016 is a Python Enhancement Proposal that defined a governance model for the Python project following Guido van Rossum’s retirement as BDFL.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PEP 8016 Target entity description: PEP 8016 is a Python Enhancement Proposal that defined a governance model for the Python project following Guido van Rossum’s retirement as BDFL.
-
A.
PEP 0
PEP 0 is the index document that lists and tracks the status of all Python Enhancement Proposals (PEPs) in the Python community.
-
B.
PEP 1
PEP 1 is the foundational Python Enhancement Proposal that defines the purpose, structure, and workflow for all other PEPs in the Python community process.
-
C.
PEP 13
PEP 13 is the Python Enhancement Proposal that defines the process and rules for selecting and operating the Python Steering Council, the core governance body of the Python project.
-
D.
PEP 634
PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
-
E.
PEPM
PEPM (Partial Evaluation and Program Manipulation) is an ACM SIGPLAN-sponsored symposium focused on research in program analysis, transformation, and generation techniques.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc61c1ef08190911d5f58c2e91189 |
completed | March 7, 2026, 6:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae895f5420819087b403e9772dce9a |
completed | March 9, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_69ae8af65eb88190b17d74e7411967cc |
completed | March 9, 2026, 8:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae8ba02cec8190917c0e17d3fedb0e |
completed | March 9, 2026, 8:58 a.m. |
Created at: March 4, 2026, 7:49 p.m.