Triple

T816118
Position Surface form Disambiguated ID Type / Status
Subject Jython E17654 entity
Predicate renamedFrom P65 FINISHED
Object JPython E17654 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: JPython | Statement: [Jython, renamedFrom, JPython]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: JPython
Context triple: [Jython, renamedFrom, JPython]
  • A. Jython chosen
    Jython is an implementation of the Python programming language that runs on the Java platform and allows seamless integration with Java code and libraries.
  • B. IronPython
    IronPython is an implementation of the Python programming language that runs on the .NET framework, allowing Python code to interoperate seamlessly with .NET libraries and applications.
  • C. 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.
  • D. Python
    Python is a monstrous serpent or dragon from Greek mythology, best known for being slain by the god Apollo at Delphi.
  • E. Python
    Python is a high-level, versatile programming language widely used for data analysis, machine learning, web development, and automation.
  • 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_69a4937bcaac8190a322524ac6f45a5a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4ab5157b08190b6c8f2fd455f261e completed March 1, 2026, 9:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7928d2ee8819091dbef1dd5272f6f completed March 4, 2026, 2:01 a.m.
Created at: March 1, 2026, 7:38 p.m.