Triple
T38639524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soufflé Datalog engine |
E938555
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | logic programming system |
C64194
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: logic programming system Context triple: [Soufflé Datalog engine, instanceOf, logic programming system]
-
A.
functional programming language
A functional programming language is a programming paradigm where computation is treated as the evaluation of mathematical functions, emphasizing immutability, first-class functions, and avoidance of side effects.
-
B.
framework in automated theorem proving
A framework in automated theorem proving is a structured environment of algorithms, data structures, and interfaces that coordinates the representation of logical formulas, the application of inference rules, and the management of proof search to automatically derive or verify theorems.
-
C.
program logic
Program logic is the structured set of rules, conditions, and sequences that determine how a program processes input, makes decisions, and produces output.
-
D.
higher-order logic theorem prover
A higher-order logic theorem prover is a software system that automatically or interactively checks, derives, and manipulates logical statements and proofs in a logic where functions and predicates can take other functions and predicates as arguments.
-
E.
Lisp machine
A Lisp machine is a specialized computer system designed to efficiently run the Lisp programming language, featuring hardware and software tightly integrated around Lisp’s execution model.
- F. None of above. chosen
Provenance (1 batch)
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_69f76ed948ec81908ce7811608a8f359 |
completed | May 3, 2026, 3:50 p.m. |
Created at: May 3, 2026, 4:32 p.m.