Triple
T13507021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Complexity of Theorem-Proving Procedures |
E321037
|
entity |
| Predicate | studiesComplexityClass |
P29141
|
FINISHED |
| Object | NP |
—
|
LITERAL FINISHED |
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: NP | Statement: [The Complexity of Theorem-Proving Procedures, studiesComplexityClass, NP]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: studiesComplexityClass Context triple: [The Complexity of Theorem-Proving Procedures, studiesComplexityClass, NP]
-
A.
complexityClassRelation
Indicates a relationship between two computational complexity classes, such as inclusion, equivalence, or separation, within the hierarchy of complexity theory.
-
B.
computationalClass
chosen
Indicates that two entities share the same computational complexity class or that one entity is categorized within a specified computational complexity class.
-
C.
hasComplexity
Indicates that something possesses a certain level or type of complexity, often in terms of structure, behavior, or difficulty.
-
D.
hasReasoningComplexity
Indicates that an action, process, or decision involves a certain level or type of cognitive or logical complexity in its reasoning.
-
E.
timeComplexity
Indicates the computational growth rate of an algorithm’s resource usage (typically time) as a function of input size.
- F. None of above.
Provenance (3 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_69d807629d6c8190998f1b9bb12d2ed0 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaf8259a08190ada13c4a3078f07d |
completed | April 12, 2026, 2:43 p.m. |
| PD | Predicate disambiguation | batch_69dbae0b63748190b5e207f84b2532ea |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:43 p.m.