Triple
T6042448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ReLU |
E134578
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Rectified Linear Unit |
E134578
|
NE 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: Rectified Linear Unit | Statement: [ReLU, fullName, Rectified Linear Unit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rectified Linear Unit Context triple: [ReLU, fullName, Rectified Linear Unit]
-
A.
ReLU
chosen
ReLU (Rectified Linear Unit) is a widely used activation function in neural networks that outputs zero for negative inputs and the input value itself for positive inputs, enabling efficient and stable training of deep models.
-
B.
LeNet
LeNet is one of the earliest convolutional neural network architectures, pioneering modern deep learning approaches to image recognition and handwritten digit classification.
-
C.
RBM
RBM is a global partnership initiative dedicated to coordinating and scaling up efforts to prevent, control, and ultimately eliminate malaria worldwide.
-
D.
Perceptrons
Perceptrons is a seminal 1969 book by Marvin Minsky and Seymour Papert that critically analyzes the capabilities and limitations of early neural network models, profoundly influencing the development of artificial intelligence and machine learning.
-
E.
ResNet
ResNet is a deep convolutional neural network architecture known for its use of residual connections to enable very deep models and achieve state-of-the-art performance in image recognition tasks.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c00876a69881908088a2626d3b2666 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c056e108fc81908775d176ff960fad |
completed | March 22, 2026, 8:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c1139793708190b14c83d4197a33a0 |
completed | March 23, 2026, 10:19 a.m. |
Created at: March 22, 2026, 4:08 p.m.