Triple
T2703894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Generative Adversarial Networks |
E59296
|
entity |
| Predicate | inputToGenerator |
P11909
|
FINISHED |
| Object | random noise vector |
—
|
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: random noise vector | Statement: [Generative Adversarial Networks, inputToGenerator, random noise vector]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inputToGenerator Context triple: [Generative Adversarial Networks, inputToGenerator, random noise vector]
-
A.
input
Indicates that one entity provides data, signals, or resources that are received or processed by another entity.
-
B.
generators
Indicates that one entity produces, creates, or brings about another entity or outcome, typically as its source or origin.
-
C.
inputType
chosen
Indicates the kind or format of data that an entity expects to receive as input in a given context.
-
D.
inputDevice
Indicates that one entity functions as a device used to provide input to another entity or system.
-
E.
generation
Indicates the relationship in which one entity produces, creates, or brings another entity into existence.
- 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.