Triple
T23791093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Teyjus |
E588091
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | LambdaProlog implementation |
C5489
|
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: LambdaProlog implementation Context triple: [Teyjus, instanceOf, LambdaProlog implementation]
-
A.
Datalog engine
A Datalog engine is a system that evaluates Datalog programs by efficiently computing logical inferences over a set of facts and rules, typically using fixpoint or bottom-up evaluation strategies.
-
B.
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.
-
C.
programming language implementation
chosen
A programming language implementation is the concrete realization of a language’s specification, including its compiler or interpreter, runtime system, and associated tools that translate and execute programs written in that language.
-
D.
theorem prover
A theorem prover is a software system or algorithm that automatically or semi-automatically checks the validity of logical statements by deriving conclusions from axioms and inference rules.
-
E.
Lisp dialect
A Lisp dialect is a specific variant of the Lisp programming language family, defined by its own syntax, semantics, and standard libraries while retaining Lisp’s core features like symbolic expressions and homoiconicity.
- F. None of above.
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_69e2490f4ad48190b690878eec3596c6 |
completed | April 17, 2026, 2:51 p.m. |
Created at: April 17, 2026, 7:17 p.m.