Triple
T4277372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TPU |
E97074
|
entity |
| Predicate | hasGeneration |
P455
|
FINISHED |
| Object | TPU v3 |
E97074
|
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: TPU v3 | Statement: [TPU, hasGeneration, TPU v3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TPU v3 Context triple: [TPU, hasGeneration, TPU v3]
-
A.
TPU
chosen
A TPU (Tensor Processing Unit) is a specialized hardware accelerator designed by Google to efficiently perform large-scale machine learning and deep learning computations.
-
B.
Tensor Processing Unit
A Tensor Processing Unit (TPU) is a specialized AI accelerator chip designed by Google to efficiently perform large-scale machine learning computations, particularly for neural networks.
-
C.
TPUs (via XLA integrations)
TPUs (via XLA integrations) are Google's specialized tensor processing units that can be used as accelerators for PyTorch models through the XLA compilation framework.
-
D.
TD3
TD3 (Twin Delayed Deep Deterministic Policy Gradient) is an off-policy deep reinforcement learning algorithm that improves upon DDPG by reducing overestimation bias and stabilizing training for continuous control tasks.
-
E.
UPVM3
UPVM3 is a French public university in Montpellier specializing in arts, humanities, and social sciences, named after the writer and philosopher Paul Valéry.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501ef1388190b0c968b069014a59 |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c7237b608190ab5aca56027344c4 |
completed | March 14, 2026, 8:37 p.m. |
Created at: March 12, 2026, 11:07 p.m.