Triple

T148153
Position Surface form Disambiguated ID Type / Status
Subject Python E3372 entity
Predicate notableVersion P3094 FINISHED
Object Python 3.10 E9268 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 3.10 | Statement: [Python, notableVersion, Python 3.10]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Python 3.10
Context triple: [Python, notableVersion, Python 3.10]
  • A. 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.
  • B. Python
    Python is a high-level, versatile programming language widely used for data analysis, machine learning, web development, and automation.
  • C. Python reference implementation (CPython) chosen
    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. TB12
    TB12 is the widely used nickname and personal brand of legendary NFL quarterback Tom Brady, often associated with his jersey number and fitness/lifestyle company.
  • E. Julia
    Julia is a high-level, high-performance programming language designed for numerical computing, data science, and scientific research, combining the ease of dynamic languages with the speed of compiled languages.
  • 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_69a252868de4819080e21c9938bfe8b6 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a25bab43608190ba5ebfbee6b5b6e4 completed Feb. 28, 2026, 3:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2d4c9ede481909a775c64222b86d3 completed Feb. 28, 2026, 11:43 a.m.
Created at: Feb. 28, 2026, 2:31 a.m.