Triple
T22386998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Waitress |
E553420
|
entity |
| Predicate | implementationLanguage |
P18654
|
FINISHED |
| Object | Python |
—
|
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 | Statement: [Waitress, implementationLanguage, Python]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Python Context triple: [Waitress, implementationLanguage, Python]
-
A.
Python
Python is a monstrous serpent or dragon from Greek mythology, best known for being slain by the god Apollo at Delphi.
-
B.
Python
chosen
Python is a high-level, versatile programming language widely used for data analysis, machine learning, web development, and automation.
-
C.
Pythonidae
Pythonidae is a family of nonvenomous constrictor snakes that includes pythons found across Africa, Asia, and Australia.
-
D.
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.
-
E.
Pythion
Pythion was an ancient city of Perrhaebia in northern Thessaly, Greece, likely known for its regional religious and strategic significance.
- 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_69e11e4cf87c8190a1ff474daec326b7 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f158304bcc81908c4c5db09a246bcc |
completed | April 29, 2026, 1 a.m. |
Created at: April 16, 2026, 8:45 p.m.