Triple
T591905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LeNet |
E17289
|
entity |
| Predicate | optimizationObjective |
P12747
|
FINISHED |
| Object | classification accuracy |
—
|
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: classification accuracy | Statement: [LeNet, optimizationObjective, classification accuracy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimizationObjective Context triple: [LeNet, optimizationObjective, classification accuracy]
-
A.
trainingObjective
chosen
Indicates the goal or target outcome that a training process is designed to achieve.
-
B.
strategicGoal
Indicates that one entity represents a long-term objective or desired outcome that another entity is intentionally aiming to achieve or align actions toward.
-
C.
educationalObjective
Indicates the intended learning goal, skill, or competency that an educational resource, activity, or program is designed to achieve.
-
D.
protectionObjective
Indicates that one entity has the goal or purpose of safeguarding, defending, or preserving another entity or its interests.
-
E.
secondaryGoal
Indicates that something serves as a subordinate or supporting objective in addition to a primary goal.
- 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_69a49379d09c8190ac7e00b24e2810b1 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49bbaf53081908eed240bed09f63b |
completed | March 1, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69a494cc13988190892ca10bd7ae9f09 |
completed | March 1, 2026, 7:34 p.m. |
Created at: March 1, 2026, 7:33 p.m.