Triple
T4277392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TPU |
E97074
|
entity |
| Predicate | offeredAs |
P1395
|
FINISHED |
| Object | TPU node |
E356779
|
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 node | Statement: [TPU, offeredAs, TPU node]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: TPU node Context triple: [TPU, offeredAs, TPU node]
-
A.
TPU
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.
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.
-
C.
Unity (Node 1)
Unity (Node 1) is the first U.S.-built module of the International Space Station, serving as a key connecting hub for other station components.
-
D.
NVIDIA Triton Inference Server
NVIDIA Triton Inference Server is an open-source, production-ready platform for serving and scaling AI model inference across GPUs and CPUs with support for multiple frameworks and deployment environments.
-
E.
Tensor Processing Unit
chosen
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.
- 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_69b5b7b3b52c8190ae7c05448faf5558 |
completed | March 14, 2026, 7:32 p.m. |
Created at: March 12, 2026, 11:07 p.m.