Triple
T18016704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | XLA |
E431013
|
entity |
| Predicate | supportsLanguage |
P2177
|
FINISHED |
| Object | HLO (High Level Optimizer) IR |
—
|
NE NERFINISHED |
How this triple was built (3 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: HLO (High Level Optimizer) IR | Statement: [XLA, supportsLanguage, HLO (High Level Optimizer) IR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: HLO (High Level Optimizer) IR Context triple: [XLA, supportsLanguage, HLO (High Level Optimizer) IR]
-
A.
MLIR
MLIR (Multi-Level Intermediate Representation) is a flexible compiler infrastructure and intermediate representation framework designed to support reusable, extensible optimizations and code generation across diverse domains and hardware targets.
-
B.
CIRCT
CIRCT is an open-source LLVM subproject that provides a set of reusable compiler infrastructure and tools for hardware design and synthesis using MLIR.
-
C.
LLVM
LLVM is a modular, reusable compiler and toolchain infrastructure project widely used for building language frontends, optimizers, and backends for diverse hardware architectures.
-
D.
Multi-Level Intermediate Representation
Multi-Level Intermediate Representation is a flexible compiler infrastructure within the LLVM project designed to support multiple abstraction levels and domain-specific optimizations in a unified IR framework.
-
E.
SPIR intermediate representation
SPIR intermediate representation is a standardized, portable intermediate language based on LLVM IR used to enable cross-platform compilation and execution of OpenCL kernels and other heterogeneous compute workloads.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: HLO (High Level Optimizer) IR Target entity description: HLO (High Level Optimizer) IR is XLA’s intermediate representation for expressing and optimizing high-level tensor computations before lowering them to hardware-specific code.
-
A.
MLIR
MLIR (Multi-Level Intermediate Representation) is a flexible compiler infrastructure and intermediate representation framework designed to support reusable, extensible optimizations and code generation across diverse domains and hardware targets.
-
B.
CIRCT
CIRCT is an open-source LLVM subproject that provides a set of reusable compiler infrastructure and tools for hardware design and synthesis using MLIR.
-
C.
LLVM
LLVM is a modular, reusable compiler and toolchain infrastructure project widely used for building language frontends, optimizers, and backends for diverse hardware architectures.
-
D.
Multi-Level Intermediate Representation
Multi-Level Intermediate Representation is a flexible compiler infrastructure within the LLVM project designed to support multiple abstraction levels and domain-specific optimizations in a unified IR framework.
-
E.
SPIR intermediate representation
SPIR intermediate representation is a standardized, portable intermediate language based on LLVM IR used to enable cross-platform compilation and execution of OpenCL kernels and other heterogeneous compute workloads.
- F. None of above. chosen
Provenance (2 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9be5d0c819097e006f32d98753a |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 10:24 a.m.