Triple
T18051444
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python official documentation |
E431933
|
entity |
| Predicate | covers |
P1393
|
FINISHED |
| Object | Python unittest module |
—
|
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: Python unittest module | Statement: [Python official documentation, covers, Python unittest module]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Python unittest module Context triple: [Python official documentation, covers, Python unittest module]
-
A.
unittest
chosen
unittest is Python’s built-in unit testing framework that provides tools for organizing tests, checking results, and automating test execution.
-
B.
ctest
ctest is CMake’s built-in testing tool used to execute and manage automated tests for software projects.
-
C.
MUnit
MUnit is a testing framework designed for Mule applications that enables developers to create, automate, and run unit and integration tests within the MuleSoft ecosystem.
-
D.
NUnit
NUnit is a popular open-source unit testing framework for .NET languages, widely used to write and run automated tests in C#.
-
E.
Test::Unit
Test::Unit is a unit testing framework for the Ruby programming language that provides a structured way to write and run automated tests.
- 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_69d8b906482481908183315b9ecf9994 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4c0fe4f1881908fa8485cb3ccfa44 |
completed | April 19, 2026, 11:48 a.m. |
Created at: April 10, 2026, 10:25 a.m.