Triple

T13320119
Position Surface form Disambiguated ID Type / Status
Subject AMD Instinct E317291 entity
Predicate includesModel P1393 FINISHED
Object AMD Instinct MI300A E1040243 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: AMD Instinct MI300A | Statement: [AMD Instinct, includesModel, AMD Instinct MI300A]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AMD Instinct MI300A
Context triple: [AMD Instinct, includesModel, AMD Instinct MI300A]
  • A. AMD Instinct MI300 series chosen
    The AMD Instinct MI300 series is a line of high-performance data center accelerators designed for AI, HPC, and advanced compute workloads.
  • B. AMD Instinct MI100
    AMD Instinct MI100 is a high-performance data center GPU accelerator from AMD designed primarily for high-performance computing (HPC) and AI workloads.
  • C. AMD Instinct MI250X
    The AMD Instinct MI250X is a high-performance data center GPU accelerator designed for exascale supercomputing, AI, and HPC workloads.
  • D. NVIDIA A100
    The NVIDIA A100 is a high-performance data center GPU designed for AI, high-performance computing, and data analytics workloads, featuring advanced Tensor Core acceleration.
  • E. NVIDIA RTX A6000
    The NVIDIA RTX A6000 is a high-end professional graphics card designed for demanding workloads like AI, data science, and advanced 3D content creation.
  • 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_69d806b4d62c81908d4ced1665414be5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d990faa95481908a7fd297959c062e completed April 11, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69f74612bab88190bf1a895b87be12c1 completed May 3, 2026, 12:56 p.m.
Created at: April 9, 2026, 9:29 p.m.