Triple
T20512750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yao’s next-bit test |
E503604
|
entity |
| Predicate | efficiencyModel |
P52249
|
FINISHED |
| Object | probabilistic polynomial-time algorithms |
—
|
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: probabilistic polynomial-time algorithms | Statement: [Yao’s next-bit test, efficiencyModel, probabilistic polynomial-time algorithms]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: efficiencyModel Context triple: [Yao’s next-bit test, efficiencyModel, probabilistic polynomial-time algorithms]
-
A.
maximumEfficiency
Indicates that an entity operates at its highest possible level of performance or productivity under given conditions.
-
B.
sampleEfficiency
Indicates how effectively a method or system learns or performs using a limited number of samples or data points.
-
C.
hasMaximumEfficiencyAt
Indicates that an entity reaches or exhibits its highest possible efficiency under a specified condition, context, or parameter value.
-
D.
performanceModel
chosen
Indicates a relationship where one entity serves as a performance model that represents, predicts, or characterizes the performance behavior of another entity.
-
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.
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_69e0b4b2aa788190ae9eb37c1d73b1f1 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e69dcd74c48190b050e25c20154c09 |
completed | April 20, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69e59fdb7ad88190924176c32a195db3 |
completed | April 20, 2026, 3:39 a.m. |
Created at: April 16, 2026, 11:36 a.m.