Triple
T22330645
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PEP 333 |
E552012
|
entity |
| Predicate | supersededBy |
P101
|
FINISHED |
| Object | PEP 3333 |
—
|
NE NERFINISHED |
How this triple was built (2 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 3333 | Statement: [PEP 333, supersededBy, PEP 3333]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 3333 Context triple: [PEP 333, supersededBy, PEP 3333]
-
A.
PEP 333
chosen
PEP 333 is the Python Enhancement Proposal that originally defined the Web Server Gateway Interface (WSGI), a standard for communication between Python web applications and web servers.
-
B.
PEP 273
PEP 273 is a Python Enhancement Proposal that introduced support for importing modules from ZIP archives, enabling Python code to be distributed and executed directly from compressed files.
-
C.
PEP 343
PEP 343 is the Python Enhancement Proposal that introduced the "with" statement and the context manager protocol to simplify resource management in Python.
-
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.
PEP 483
PEP 483 is a Python Enhancement Proposal that lays out the theoretical foundations and design principles for Python’s type hinting and generic types system.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e482f788190b78d1588fc26d606 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1577a9c348190b8662142afa832be |
completed | April 29, 2026, 12:57 a.m. |
Created at: April 16, 2026, 8:43 p.m.