Triple

T452311
Position Surface form Disambiguated ID Type / Status
Subject SQL Server E7155 entity
Predicate supportsLanguage P2177 FINISHED
Object Python E3372 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: Python | Statement: [SQL Server, supportsLanguage, Python]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Python
Context triple: [SQL Server, supportsLanguage, Python]
  • A. Python chosen
    Python is a high-level, versatile programming language widely used for data analysis, machine learning, web development, and automation.
  • B. PyPy
    PyPy is a high-performance alternative Python interpreter featuring a Just-In-Time (JIT) compiler designed to significantly speed up the execution of Python programs.
  • C. Python Consortium
    The Python Consortium was an early industry-backed organization that coordinated corporate support and development for the Python programming language before its role was taken over by the Python Software Foundation.
  • D. Jython
    Jython is an implementation of the Python programming language that runs on the Java platform and allows seamless integration with Java code and libraries.
  • E. Python Software Foundation
    The Python Software Foundation is a non-profit organization that manages the development, licensing, and community support of the Python programming language.
  • 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_69a2e7e4676c81909ea0dbdecac0687c completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ef854f7481909dc2207faf0327ec completed Feb. 28, 2026, 1:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69a44802e858819081a0b5b98bb25bce completed March 1, 2026, 2:06 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.