Triple
T26966709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RP |
E679190
|
entity |
| Predicate | relationToZPP |
P62369
|
FINISHED |
| Object | ZPP equals RP ∩ coRP |
—
|
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: ZPP equals RP ∩ coRP | Statement: [RP, relationToZPP, ZPP equals RP ∩ coRP]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relationToZPP Context triple: [RP, relationToZPP, ZPP equals RP ∩ coRP]
-
A.
relationToNPCompleteness
Indicates that the subject has a defined conceptual or formal connection to the notion of NP-completeness (e.g., being NP-complete, related to NP-complete problems, or used in reasoning about NP-completeness).
-
B.
complexityClassRelation
chosen
Indicates a relationship between two computational complexity classes, such as inclusion, equivalence, or separation, within the hierarchy of complexity theory.
-
C.
relationToBPP
Indicates the specific type of relationship or association an entity has to a designated BPP (e.g., as owner, member, participant, or related party).
-
D.
relationToVonNeumann
Indicates a relationship in which one entity is connected or related in some specified way to John von Neumann.
-
E.
pseudoPolynomialTime
Indicates that the time complexity of an algorithm is polynomial in the numeric value of the input (e.g., the magnitude of numbers) rather than in the length of the input’s encoding.
- 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_69eeeb4f3a448190b1e94b2d4776c16e |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69ff1e3e13c08190bb8990c44716b746 |
completed | May 9, 2026, 11:45 a.m. |
| PD | Predicate disambiguation | batch_69ff1dfcaf2c8190aaf2b428d57b7782 |
completed | May 9, 2026, 11:43 a.m. |
Created at: April 27, 2026, 6:36 a.m.