Triple

T4599957
Position Surface form Disambiguated ID Type / Status
Subject Python scientific stack E100298 entity
Predicate hasComponent P35 FINISHED
Object Cython E96633 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: Cython | Statement: [Python scientific stack, hasComponent, Cython]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cython
Context triple: [Python scientific stack, hasComponent, Cython]
  • A. Cython chosen
    Cython is a programming language and compiler that extends Python with static typing and direct C/C++ integration to generate fast, optimized extension modules.
  • B. 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.
  • C. Python reference implementation (CPython)
    Python reference implementation (CPython) is the original and most widely used implementation of the Python programming language, written in C and serving as the de facto standard for Python behavior and compatibility.
  • D. Pythonidae
    Pythonidae is a family of nonvenomous constrictor snakes that includes pythons found across Africa, Asia, and Australia.
  • E. CuPy
    CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
  • 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_69bd43cbc014819098b45f435908f88a completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd5971f448819090f6e76c7d3ffc2d completed March 20, 2026, 2:28 p.m.
NED1 Entity disambiguation (via context triple) batch_69bdfa54bb0c819081265a6d159ad790 completed March 21, 2026, 1:54 a.m.
Created at: March 20, 2026, 1:11 p.m.