Triple
T36490261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reformer architecture |
E899032
|
entity |
| Predicate | memoryOptimizationTechnique |
P90443
|
FINISHED |
| Object | reversible residual computation |
—
|
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: reversible residual computation | Statement: [Reformer architecture, memoryOptimizationTechnique, reversible residual computation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: memoryOptimizationTechnique Context triple: [Reformer architecture, memoryOptimizationTechnique, reversible residual computation]
-
A.
memoryEfficiencyReason
Indicates that there is an explanation or cause for why something is efficient in its use of memory resources.
-
B.
canBeOptimizedFor
Indicates that one entity is capable of being improved or adjusted to perform better with respect to another specified criterion, context, or target.
-
C.
powerOptimizationFor
Indicates a relationship where one entity is used to improve, manage, or optimize the power consumption or power efficiency of another entity.
-
D.
optimize
Indicates improving a process, system, or outcome to achieve the best possible performance or efficiency under given constraints.
-
E.
memoryStrategy
chosen
Indicates the method or approach an entity uses to encode, store, or retrieve information from memory.
- 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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7be9d07ac8190adf796cbef60daf6 |
completed | May 3, 2026, 9:31 p.m. |
| PD | Predicate disambiguation | batch_69f7bccf05bc8190b61fdb2b2a315811 |
completed | May 3, 2026, 9:23 p.m. |
Created at: May 3, 2026, 4:10 p.m.