Triple
T7600487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SU(3) |
E179969
|
entity |
| Predicate | hasFundamentalRepresentationDimension |
P78094
|
FINISHED |
| Object | 3 |
—
|
LITERAL 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: 3 | Statement: [SU(3), hasFundamentalRepresentationDimension, 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFundamentalRepresentationDimension Context triple: [SU(3), hasFundamentalRepresentationDimension, 3]
-
A.
hasIrreducibleRepresentationOfDimension
Indicates that an entity possesses an irreducible representation whose vector space has the specified dimension.
-
B.
minimalFaithfulComplexRepresentationDimension
Indicates the smallest possible dimension of a complex vector space in which a faithful (injective) representation of the given structure can be realized.
-
C.
hasMinimalFaithfulComplexRepresentationDegree
Indicates the smallest dimension in which a faithful (injective) complex linear representation of the given object exists.
-
D.
hasMinimalFaithfulRealRepresentationDegree
Indicates the smallest dimension in which a group or structure can be represented by real matrices in a way that is both injective (faithful) and structure-preserving.
-
E.
minimalFaithfulLinearRepresentationDegree
Indicates the smallest dimension of a faithful linear representation needed to represent the given structure as linear transformations on a vector space.
- F. None of above. chosen
Provenance (4 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_69c69f3487ec8190bf7acdf2dd91e6d6 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9d9c55c8190841f3bf3225c096a |
completed | March 27, 2026, 9:42 p.m. |
| PD | Predicate disambiguation | batch_69c6f4e2e42c8190afc802c4796c9cc2 |
completed | March 27, 2026, 9:21 p.m. |
| PDg | Predicate description generation | batch_69c6f8184bb08190b2f70545a6aa277c |
completed | March 27, 2026, 9:35 p.m. |
Created at: March 27, 2026, 3:53 p.m.