Triple
T1180425
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | deep feedforward networks |
E25122
|
entity |
| Predicate | canUseLossFunction |
P9928
|
FINISHED |
| Object | mean squared error |
—
|
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: mean squared error | Statement: [deep feedforward networks, canUseLossFunction, mean squared error]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canUseLossFunction Context triple: [deep feedforward networks, canUseLossFunction, mean squared error]
-
A.
canUse
chosen
Indicates that one entity has the ability, permission, or suitability to make use of another entity or resource.
-
B.
usesComputationMethod
Indicates that an entity performs its processing or decision-making by applying a specified computational method or algorithm.
-
C.
hasUseCase
Indicates that one entity is employed, applied, or utilized as a solution or method to address a particular need, problem, or scenario associated with another entity.
-
D.
canBeUsedOver
Indicates that one entity is suitable or valid for use in place of, or in relation to, another entity.
-
E.
canBeFineTuned
Indicates that one entity (typically a model or system) is capable of being further trained or adjusted using additional data or tasks to improve or specialize its behavior.
- 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_69a494267b4c819088c97a59182bf56a |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd32c5f48190b4e2d39fa052cbb7 |
completed | March 1, 2026, 10:26 p.m. |
| PD | Predicate disambiguation | batch_69a4bb59ca6c81908597a81646674aaa |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:45 p.m.