Triple
T7197433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gracemont |
E168650
|
entity |
| Predicate | supportsInstructionSetExtension |
P203
|
FINISHED |
| Object |
FMA3
FMA3 is an x86 instruction set extension that provides fused multiply-add operations to improve floating-point performance and efficiency in modern processors.
|
E648231
|
NE FINISHED |
How this triple was built (4 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: FMA3 | Statement: [Gracemont, supportsInstructionSetExtension, FMA3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FMA3 Context triple: [Gracemont, supportsInstructionSetExtension, FMA3]
-
A.
FMU
FMU is a public university in Florence, South Carolina, known for its liberal arts and professional programs.
-
B.
F32
F32 is BMW’s internal model code for the first-generation 4 Series coupe, introduced as a sporty, premium compact executive car.
-
C.
FAMa
FAMa is the national military force of Mali responsible for the country’s defense and security operations.
-
D.
F-3
F-3 is a three-quarter-ton model in Ford’s first-generation postwar F-Series pickup truck lineup, known as the “Bonus-Built” trucks produced in the late 1940s and early 1950s.
-
E.
FAM
FAM is the acronym commonly used to refer to the Mexican Air Force, the aerial warfare branch of Mexico’s armed forces.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: FMA3 Triple: [Gracemont, supportsInstructionSetExtension, FMA3]
Generated description
FMA3 is an x86 instruction set extension that provides fused multiply-add operations to improve floating-point performance and efficiency in modern processors.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FMA3 Target entity description: FMA3 is an x86 instruction set extension that provides fused multiply-add operations to improve floating-point performance and efficiency in modern processors.
-
A.
FMU
FMU is a public university in Florence, South Carolina, known for its liberal arts and professional programs.
-
B.
F32
F32 is BMW’s internal model code for the first-generation 4 Series coupe, introduced as a sporty, premium compact executive car.
-
C.
FAMa
FAMa is the national military force of Mali responsible for the country’s defense and security operations.
-
D.
F-3
F-3 is a three-quarter-ton model in Ford’s first-generation postwar F-Series pickup truck lineup, known as the “Bonus-Built” trucks produced in the late 1940s and early 1950s.
-
E.
FAM
FAM is the acronym commonly used to refer to the Mexican Air Force, the aerial warfare branch of Mexico’s armed forces.
- F. None of above. chosen
Provenance (5 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_69c68a5376748190bb500f03df86e93e |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6e92a5d288190955f703470e75bf3 |
completed | March 27, 2026, 8:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7bfa6be648190950f682eaaeb1a18 |
completed | March 28, 2026, 11:46 a.m. |
| NEDg | Description generation | batch_69c7c01e84388190858d34a6a047bf63 |
completed | March 28, 2026, 11:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7c0a9eb0c819080cda73d67e84fe9 |
completed | March 28, 2026, 11:51 a.m. |
Created at: March 27, 2026, 2:51 p.m.