Triple
T3071389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adam Vinatieri |
E64030
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object | Automatic Adam |
E64030
|
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: Automatic Adam | Statement: [Adam Vinatieri, nickname, Automatic Adam]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Automatic Adam Context triple: [Adam Vinatieri, nickname, Automatic Adam]
-
A.
Automatic Adam
chosen
Automatic Adam is the nickname of Adam Vinatieri, a legendary NFL placekicker renowned for his clutch, game-winning field goals in high-pressure situations.
-
B.
Adam: A Method for Stochastic Optimization
"Adam: A Method for Stochastic Optimization" is a highly influential machine learning paper that introduces the Adam optimizer, a widely used adaptive gradient-based optimization algorithm for training deep neural networks.
-
C.
Adam optimizer
The Adam optimizer is a popular stochastic gradient descent method in machine learning that adaptively adjusts learning rates for each parameter using estimates of first and second moments of gradients.
-
D.
Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
-
E.
Progressive GAN
Progressive GAN is a generative adversarial network architecture that grows both the generator and discriminator layers progressively during training to produce high-resolution, high-quality synthetic images.
- 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_69ad857a8aec8190bfdfd9c14554ac5a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada14b366881908e2ca104e5f38251 |
completed | March 8, 2026, 4:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1f87f6a3881908ae313f62ff13159 |
completed | March 11, 2026, 11:19 p.m. |
Created at: March 8, 2026, 3:02 p.m.