Triple
T4277978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | pip |
E97086
|
entity |
| Predicate | implements |
P1417
|
FINISHED |
| Object |
PEP 503
PEP 503 is a Python Enhancement Proposal that defines the simple repository API used by package installers like pip to discover and download Python packages.
|
E426701
|
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 503 | Statement: [pip, implements, PEP 503]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 503 Context triple: [pip, implements, PEP 503]
-
A.
Python Packaging Authority
The Python Packaging Authority is a working group that oversees and develops key tools, standards, and infrastructure for packaging and distributing Python software.
-
B.
PEP 634
PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
-
C.
PEP 695
PEP 695 is a Python Enhancement Proposal that introduces a new, more concise syntax for type parameter declarations to improve the language’s support for generics and static typing.
-
D.
PEP 635
PEP 635 is a Python Enhancement Proposal that provides a detailed rationale and motivation for the structural pattern matching feature introduced in Python 3.10.
-
E.
PEP 636
PEP 636 is a Python Enhancement Proposal that serves as a tutorial-style guide to the structural pattern matching feature introduced in Python 3.10.
- 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 503 Triple: [pip, implements, PEP 503]
Generated description
PEP 503 is a Python Enhancement Proposal that defines the simple repository API used by package installers like pip to discover and download Python packages.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PEP 503 Target entity description: PEP 503 is a Python Enhancement Proposal that defines the simple repository API used by package installers like pip to discover and download Python packages.
-
A.
Python Packaging Authority
The Python Packaging Authority is a working group that oversees and develops key tools, standards, and infrastructure for packaging and distributing Python software.
-
B.
PEP 634
PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
-
C.
PEP 695
PEP 695 is a Python Enhancement Proposal that introduces a new, more concise syntax for type parameter declarations to improve the language’s support for generics and static typing.
-
D.
PEP 635
PEP 635 is a Python Enhancement Proposal that provides a detailed rationale and motivation for the structural pattern matching feature introduced in Python 3.10.
-
E.
PEP 636
PEP 636 is a Python Enhancement Proposal that serves as a tutorial-style guide to the structural pattern matching feature introduced in Python 3.10.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501ef1388190b0c968b069014a59 |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7b3b52c8190ae7c05448faf5558 |
completed | March 14, 2026, 7:32 p.m. |
| NEDg | Description generation | batch_69b5b95126bc8190bdd9e5317f2233b0 |
completed | March 14, 2026, 7:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5b9d2ae6481908ba07d67b03c3a09 |
completed | March 14, 2026, 7:41 p.m. |
Created at: March 12, 2026, 11:07 p.m.