Triple
T18015742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bottle |
E430994
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object | Python 2 |
—
|
NE NERFINISHED |
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 2 | Statement: [Bottle, supports, Python 2]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Python 2 Context triple: [Bottle, supports, Python 2]
-
A.
Python 2.0
Python 2.0 is a major early release of the Python programming language that introduced significant new features and improvements, helping to shape Python’s modern development ecosystem.
-
B.
Pythonidae
Pythonidae is a family of nonvenomous constrictor snakes that includes pythons found across Africa, Asia, and Australia.
-
C.
Python 3.x
Python 3.x is the major, actively developed series of the Python programming language that introduced significant improvements and changes over Python 2, including cleaner syntax, better Unicode support, and a more consistent standard library.
-
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
chosen
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 (2 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.