Triple
T22445545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MiniScheme |
E554854
|
entity |
| Predicate | inspired |
P9
|
FINISHED |
| Object | TinyScheme |
—
|
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: TinyScheme | Statement: [MiniScheme, inspired, TinyScheme]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TinyScheme Context triple: [MiniScheme, inspired, TinyScheme]
-
A.
TinyScheme
chosen
TinyScheme is a lightweight, embeddable implementation of the Scheme programming language designed for easy integration into applications.
-
B.
MiniScheme
MiniScheme is a minimalist implementation of the Scheme programming language that served as a conceptual and design inspiration for TinyScheme.
-
C.
Chez Scheme
Chez Scheme is a high-performance, optimizing implementation of the Scheme programming language widely used for both research and production systems.
-
D.
PLT Scheme
PLT Scheme is the original name of the programming language and environment that later evolved into Racket, known for its powerful support of functional and language-oriented programming.
-
E.
MIT Scheme
MIT Scheme is a long-standing, feature-rich implementation of the Scheme programming language developed at the Massachusetts Institute of Technology, often used for teaching and research in computer science.
- 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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b46e8ac8190bfa8c611ffcba822 |
completed | April 29, 2026, 1:13 a.m. |
Created at: April 16, 2026, 8:47 p.m.