Triple
T17674753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spec# |
E440617
|
entity |
| Predicate | uses |
P98
|
FINISHED |
| Object | Z3 theorem prover |
—
|
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: Z3 theorem prover | Statement: [Spec#, uses, Z3 theorem prover]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Z3 theorem prover Context triple: [Spec#, uses, Z3 theorem prover]
-
A.
Z3 SMT solver
Z3 SMT solver is a high-performance Satisfiability Modulo Theories (SMT) solver developed at Microsoft Research, widely used in program verification, formal methods, and automated reasoning.
-
B.
Z3: An Efficient SMT Solver
Z3: An Efficient SMT Solver is a high-performance satisfiability modulo theories (SMT) solver widely used in program verification, formal methods, and automated reasoning.
-
C.
Yices
Yices is a high-performance Satisfiability Modulo Theories (SMT) solver widely used in formal verification and automated reasoning.
-
D.
Z3
chosen
Z3 is a high-performance theorem prover and SMT (Satisfiability Modulo Theories) solver developed by Microsoft Research, widely used in formal verification, program analysis, and automated reasoning.
-
E.
Boolector
Boolector is an efficient SMT solver specialized in bit-vectors, arrays, and uninterpreted functions, widely used in formal verification and model checking.
- 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_69d8b9e87e18819087104a44dc4dc5b1 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e46f6ba22081909e2099490c047378 |
completed | April 19, 2026, 6 a.m. |
Created at: April 10, 2026, 10 a.m.