Triple
T22444130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karma |
E554823
|
entity |
| Predicate | supportsLanguage |
P2177
|
FINISHED |
| Object | CoffeeScript |
—
|
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: CoffeeScript | Statement: [Karma, supportsLanguage, CoffeeScript]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CoffeeScript Context triple: [Karma, supportsLanguage, CoffeeScript]
-
A.
CoffeeScript
chosen
CoffeeScript is a programming language that compiles to JavaScript, offering a more concise, Python- and Ruby-like syntax for writing web application code.
-
B.
Moonscript
Moonscript is a high-level programming language that compiles to Lua, offering a cleaner, more expressive syntax while targeting the Lua runtime.
-
C.
PureScript
PureScript is a strongly-typed, purely functional programming language that compiles to JavaScript and is heavily inspired by Haskell.
-
D.
Sass
Sass is a popular CSS preprocessor language that extends standard CSS with features like variables, nesting, mixins, and functions to enable more powerful and maintainable styling.
-
E.
Haml
Haml is a whitespace-sensitive templating language for Ruby that provides a clean, indentation-based syntax for generating HTML.
- 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_69f15ae517208190924a7968723f55ef |
completed | April 29, 2026, 1:12 a.m. |
Created at: April 16, 2026, 8:47 p.m.