Triple

T2629461
Position Surface form Disambiguated ID Type / Status
Subject OpenACC E59597 entity
Predicate competesWith P1375 FINISHED
Object OpenMP target offload E59596 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: OpenMP target offload | Statement: [OpenACC, competesWith, OpenMP target offload]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: OpenMP target offload
Context triple: [OpenACC, competesWith, OpenMP target offload]
  • A. OpenMP chosen
    OpenMP is an application programming interface that supports multi-platform shared-memory parallel programming in C, C++, and Fortran.
  • B. OpenACC
    OpenACC is a directive-based parallel programming standard designed to simplify the development of portable, high-performance code on heterogeneous systems such as GPUs and multicore CPUs.
  • C. OpenCL
    OpenCL is an open, cross-platform framework for writing programs that execute across heterogeneous systems including CPUs, GPUs, and other processors.
  • D. OMPS
    OMPS (Ozone Mapping and Profiler Suite) is a satellite-based instrument system designed to measure global ozone distribution and monitor atmospheric ozone layer changes from orbit.
  • E. CUDA Fortran
    CUDA Fortran is an extension of the Fortran programming language that enables developers to write and run parallel code on NVIDIA GPUs using the CUDA architecture.
  • 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_69ab4ac8596c8190b34997e73d9e991c completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abd8c452508190b02e1630d725497a completed March 7, 2026, 7:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69af90a44a348190b8b49b37418dd94b completed March 10, 2026, 3:31 a.m.
Created at: March 6, 2026, 9:50 p.m.