Triple
T12267261
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Conditional GAN |
E292378
|
entity |
| Predicate | typicalLossFunction |
P31982
|
FINISHED |
| Object | adversarial loss |
—
|
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: adversarial loss | Statement: [Conditional GAN, typicalLossFunction, adversarial loss]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalLossFunction Context triple: [Conditional GAN, typicalLossFunction, adversarial loss]
-
A.
usesLossFunction
chosen
Indicates that one entity employs a particular loss function as part of its optimization or learning process.
-
B.
lossType
Indicates the specific category or nature of a loss associated with an entity or event.
-
C.
typicalDefaultLearningRate
Indicates the standard or commonly used learning rate value typically applied by default in a learning or optimization process.
-
D.
trainingObjective
Indicates the goal or target outcome that a training process is designed to achieve.
-
E.
typicalFunction
Indicates that something serves as the usual or characteristic function or role of an entity.
- 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_69d6ab6856488190b5d31178d5015f8e |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d9380a5e78819086bd4dfe9a83d1f5 |
completed | April 10, 2026, 5:48 p.m. |
| PD | Predicate disambiguation | batch_69d91c4a66cc819083ce6fcaf5042af6 |
completed | April 10, 2026, 3:50 p.m. |
Created at: April 8, 2026, 9:52 p.m.