Triple
T32603742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Naor–Reingold pseudorandom function |
E833448
|
entity |
| Predicate | efficiency |
P174638
|
FINISHED |
| Object | simple |
—
|
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: simple | Statement: [Naor–Reingold pseudorandom function, efficiency, simple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: efficiency Context triple: [Naor–Reingold pseudorandom function, efficiency, simple]
-
A.
maximumEfficiency
Indicates that an entity operates at its highest possible level of performance or productivity under given conditions.
-
B.
netEfficiency
Indicates the overall effectiveness of a system or process after accounting for all losses, typically expressed as the ratio of useful output to total input.
-
C.
moreEfficientThan
Indicates that one entity performs a task or uses resources with greater efficiency than another entity.
-
D.
sampleEfficiency
Indicates how effectively a method or system learns or performs using a limited number of samples or data points.
-
E.
thermalEfficiency
Indicates how effectively an energy conversion process transforms input energy into useful output work or heat, typically expressed as a ratio or percentage.
- F. None of above. chosen
Provenance (4 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_69f3492ab63c8190aec24d5003b47c29 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c6c4b418819089ad5fb4d768a129 |
completed | May 3, 2026, 3:53 a.m. |
| PD | Predicate disambiguation | batch_69f6bd2c138481908afa3ee3e91f8900 |
completed | May 3, 2026, 3:12 a.m. |
| PDg | Predicate description generation | batch_69f6c2df27ec8190912ec8eb488836d0 |
completed | May 3, 2026, 3:37 a.m. |
Created at: May 1, 2026, 1:05 a.m.