Triple
T14721023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valiant–Vazirani theorem |
E345812
|
entity |
| Predicate | reductionType |
P3630
|
FINISHED |
| Object | randomized polynomial-time reduction |
—
|
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: randomized polynomial-time reduction | Statement: [Valiant–Vazirani theorem, reductionType, randomized polynomial-time reduction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: reductionType Context triple: [Valiant–Vazirani theorem, reductionType, randomized polynomial-time reduction]
-
A.
reduces
Indicates that one entity causes a decrease in the amount, intensity, degree, or impact of another entity.
-
B.
reducedRepresentationOf
Indicates that one entity is a simplified, compressed, or lower-detail version of another entity while preserving its essential information or structure.
-
C.
reducesTo
chosen
Indicates that one expression, structure, or state can be transformed or simplified into another, typically more basic or canonical, form.
-
D.
reducedNumberOf
Indicates that the subject has a smaller quantity or count of the specified object or attribute compared to a prior state or reference level.
-
E.
targetReduction
Indicates a relationship where one entity is intended or expected to decrease, diminish, or lessen another entity by a specified amount or proportion.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25d56fc8190871873ca55d49272 |
completed | April 14, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69de657e174481909da0437556334a04 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:29 a.m.