Triple
T22444834
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | illumos |
E554837
|
entity |
| Predicate | includesComponent |
P1393
|
FINISHED |
| Object | FMA |
—
|
NE NERFINISHED |
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: FMA | Statement: [illumos, includesComponent, FMA]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FMA Context triple: [illumos, includesComponent, FMA]
-
A.
FMA
chosen
FMA is an acronym commonly used for Fault Management Architecture, a system framework for detecting, diagnosing, and resolving hardware and software faults in computing environments.
-
B.
FMA
FMA is a Catholic religious institute of women, formally known as the Daughters of Mary Help of Christians, dedicated to education and youth ministry in the Salesian tradition.
-
C.
FMA3
FMA3 is an x86 instruction set extension that provides fused multiply-add operations to improve floating-point performance and efficiency in modern processors.
-
D.
FAMa
FAMa is the national military force of Mali responsible for the country’s defense and security operations.
-
E.
FUMA
FUMA is the art museum of Flinders University in South Australia, known for its diverse collections of Australian, Aboriginal, and international art.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15ae517208190924a7968723f55ef |
completed | April 29, 2026, 1:12 a.m. |
Created at: April 16, 2026, 8:47 p.m.