Triple
T11216041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PEP 484 |
E265441
|
entity |
| Predicate | defines |
P264
|
FINISHED |
| Object | typing.Mapping |
E887713
|
NE FINISHED |
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: typing.Mapping | Statement: [PEP 484, defines, typing.Mapping]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: typing.Mapping Context triple: [PEP 484, defines, typing.Mapping]
-
A.
typer
Typer is a modern, user-friendly Python library for building command-line interfaces, created by Sebastián Ramírez (tiangolo), the author of FastAPI.
-
B.
Python typing module
chosen
The Python typing module is a standard library component that adds support for type hints and static type checking to Python code, enabling clearer interfaces and improved tooling.
-
C.
MAPER
MAPER is the official abbreviation for the Spanish Army’s Personnel Command (Mando de Personal), the body responsible for managing military human resources and related administrative functions.
-
D.
PEP 484
PEP 484 is the Python Enhancement Proposal that introduced a standard for type hints in Python, forming the basis of the language’s static typing ecosystem.
-
E.
FMAP
FMAP is the federal government’s share of Medicaid program costs, used to determine how much federal funding each state receives for eligible medical services.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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. |
Created at: April 8, 2026, 9:30 p.m.