Triple
T7666062
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NP-completeness |
E173625
|
entity |
| Predicate | usesReductionType |
P3630
|
FINISHED |
| Object | polynomial-time many-one 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: polynomial-time many-one reduction | Statement: [NP-completeness, usesReductionType, polynomial-time many-one reduction]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesReductionType Context triple: [NP-completeness, usesReductionType, polynomial-time many-one reduction]
-
A.
reduces
Indicates that one entity causes a decrease in the amount, intensity, degree, or impact of another entity.
-
B.
reducesTo
chosen
Indicates that one expression, structure, or state can be transformed or simplified into another, typically more basic or canonical, form.
-
C.
isUsedUnder
Indicates that one entity is utilized or applied within the context, conditions, or framework defined by another entity.
-
D.
usesOpticsType
Indicates that one entity employs or is characterized by a specific type of optical system or technology.
-
E.
canUseFocalReducer
Indicates that an entity is capable of using a focal reducer in relation to another entity or context.
- 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_69c699562484819086752091e3164a27 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7063dab1881909598b04999b8b690 |
completed | March 27, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69c7015f7430819099d3ea2781b7cee2 |
completed | March 27, 2026, 10:14 p.m. |
Created at: March 27, 2026, 4 p.m.