Triple
T148088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python |
E3372
|
entity |
| Predicate | implementation |
P1417
|
FINISHED |
| Object |
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.
|
E17653
|
NE FINISHED |
How this triple was built (4 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: PyPy | Statement: [Python, implementation, PyPy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PyPy Context triple: [Python, implementation, PyPy]
-
A.
Python
Python is a high-level, versatile programming language widely used for data analysis, machine learning, web development, and automation.
-
B.
P5
P5 is a common abbreviation for the “Power Five,” the group of the five most prominent NCAA Division I college athletic conferences in the United States.
-
C.
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.
-
D.
Pixel
Pixel is Google's flagship line of Android smartphones known for their clean software experience and advanced camera capabilities.
-
E.
UPY
UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: PyPy Triple: [Python, implementation, PyPy]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PyPy Target entity description: 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.
-
A.
Python
Python is a high-level, versatile programming language widely used for data analysis, machine learning, web development, and automation.
-
B.
P5
P5 is a common abbreviation for the “Power Five,” the group of the five most prominent NCAA Division I college athletic conferences in the United States.
-
C.
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.
-
D.
Pixel
Pixel is Google's flagship line of Android smartphones known for their clean software experience and advanced camera capabilities.
-
E.
UPY
UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
- F. None of above. chosen
Provenance (5 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_69a2c27754a881908ef5a96e05e515e3 |
completed | Feb. 28, 2026, 10:24 a.m. |
| NEDg | Description generation | batch_69a2c37177348190857d52872e6ab393 |
completed | Feb. 28, 2026, 10:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2c3c512f08190bb87f874524b1616 |
completed | Feb. 28, 2026, 10:30 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.