Triple
T18204714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | OPT |
E435873
|
entity |
| Predicate | trainingComputeOptimization |
P64004
|
FINISHED |
| Object | efficiency-focused implementation |
—
|
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: efficiency-focused implementation | Statement: [OPT, trainingComputeOptimization, efficiency-focused implementation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trainingComputeOptimization Context triple: [OPT, trainingComputeOptimization, efficiency-focused implementation]
-
A.
trainingCompute
Indicates the amount or configuration of computational resources used to train a model or system.
-
B.
supportsOptimizationAlgorithm
Indicates that one entity is capable of running, integrating, or being compatible with a specified optimization algorithm.
-
C.
trainingObjective
Indicates the goal or target outcome that a training process is designed to achieve.
-
D.
computationalCost
chosen
Indicates the amount of computing resources (such as time, memory, or processing power) required to perform a given operation or process.
-
E.
trainingModel
Indicates that an entity is engaged in the process of teaching, adjusting, or optimizing a model using data or experience.
- 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_69d8b90dba6481908e119eb9aa4ca0cb |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4e222831081908f7d5500424e3acb |
completed | April 19, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4332155d88190b106d0dceb4554af |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:32 a.m.