Triple
T17557374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python packaging ecosystem |
E427621
|
entity |
| Predicate | includesConcept |
P531
|
FINISHED |
| Object | PEP 621 |
—
|
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 621 | Statement: [Python packaging ecosystem, includesConcept, PEP 621]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 621 Context triple: [Python packaging ecosystem, includesConcept, PEP 621]
-
A.
PEP 621
chosen
PEP 621 is a Python Enhancement Proposal that standardizes how project metadata is declared in pyproject.toml, simplifying and unifying package configuration for Python packaging tools.
-
B.
PEP 622
PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
-
C.
PEP 618
PEP 618 is a Python Enhancement Proposal that introduced the `strict` parameter to the built-in `zip` function, enabling stricter handling of iterables with mismatched lengths.
-
D.
PEP 657
PEP 657 is a Python enhancement proposal that improves error reporting by adding fine-grained location information (such as per-expression line and column data) to tracebacks.
-
E.
PEP 613
PEP 613 is a Python Enhancement Proposal that introduces the `TypeAlias` annotation to clearly declare and distinguish type aliases in Python’s type hinting 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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4562413d08190acaa5272046d3626 |
completed | April 19, 2026, 4:12 a.m. |
Created at: April 10, 2026, 5:50 a.m.