Triple
T17521017
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TensorFlow SavedModel (via conversion) |
E426677
|
entity |
| Predicate | canBeOptimizedFor |
P127766
|
FINISHED |
| Object | client-side inference |
—
|
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: client-side inference | Statement: [TensorFlow SavedModel (via conversion), canBeOptimizedFor, client-side inference]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canBeOptimizedFor Context triple: [TensorFlow SavedModel (via conversion), canBeOptimizedFor, client-side inference]
-
A.
supportsOptimizationAlgorithm
Indicates that one entity is capable of running, integrating, or being compatible with a specified optimization algorithm.
-
B.
lessOptimizedFor
Indicates that one entity is designed, configured, or adapted to perform a task or function with lower efficiency or effectiveness compared to another entity.
-
C.
canBeImplementedWith
Indicates that one entity is capable of being realized, executed, or fulfilled through the use or application of another entity.
-
D.
optimizationType
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
E.
optimizationLevel
Indicates the degree or intensity to which a process, system, or solution has been refined to improve its performance or efficiency.
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d23cf08190925510344fa36f57 |
completed | April 19, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f8b9888190aa8a45e09acf4319 |
completed | April 18, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69e3bbb37d148190b7f38599c06594ee |
completed | April 18, 2026, 5:13 p.m. |
Created at: April 10, 2026, 5:49 a.m.