Triple
T2703896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Generative Adversarial Networks |
E59296
|
entity |
| Predicate | inputToDiscriminator |
P21226
|
FINISHED |
| Object | real samples |
—
|
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: real samples | Statement: [Generative Adversarial Networks, inputToDiscriminator, real samples]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inputToDiscriminator Context triple: [Generative Adversarial Networks, inputToDiscriminator, real samples]
-
A.
judgeFrom
Indicates that one entity forms an opinion, evaluation, or conclusion about something based on another specified source, basis, or perspective.
-
B.
classificationConsensus
Indicates that multiple agents or sources agree on the same classification or category assignment for a given entity or item.
-
C.
resultRecognizedBy
Indicates that a particular result is formally acknowledged, validated, or accepted by a specified recognizing entity.
-
D.
trainingDataType
chosen
Indicates the type or category of data used for training a model, system, or process.
-
E.
classificationStart
Indicates the point in time or process at which a classification or categorization of an entity begins.
- 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_69ab4ac66bc88190b9e4afa5fc843f72 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda5011bc8190ae4e41da391e759c |
completed | March 7, 2026, 7:57 a.m. |
| PD | Predicate disambiguation | batch_69abd82062988190b4292f242ad70b2c |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:55 p.m.