Triple

T18799881
Position Surface form Disambiguated ID Type / Status
Subject PySide2 E459731 entity
Predicate provides P490 FINISHED
Object QtSql 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: QtSql module | Statement: [PySide2, provides, QtSql module]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: QtSql module
Context triple: [PySide2, provides, QtSql module]
  • A. Qt SQL chosen
    Qt SQL is a Qt framework module that provides a unified, high-level API for accessing and manipulating SQL databases across multiple backends.
  • B. QtQml module
    The QtQml module is a Qt framework component that enables building applications with the QML language, supporting declarative UI design and JavaScript integration.
  • C. SQLite
    SQLite is a lightweight, self-contained, serverless SQL database engine widely embedded in applications, operating systems, and devices.
  • D. Qt
    Qt is a cross-platform application development framework widely used for building graphical user interfaces and multi-platform software in C++.
  • E. ODBC
    ODBC (Open Database Connectivity) is a standard API that enables applications to access and query data from a wide variety of relational and non-relational database management systems using a common interface.
  • 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_69d8d398c7d4819091cb2f7e48948aeb completed April 10, 2026, 10:40 a.m.
NER Named-entity recognition batch_69e5a02273b481909bc250144a0ace32 completed April 20, 2026, 3:40 a.m.
Created at: April 10, 2026, 11:53 a.m.