Triple
T23372447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GL(n,ℝ) |
E593509
|
entity |
| Predicate | isDenseIn |
P152469
|
FINISHED |
| Object | M_n(ℝ) |
—
|
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: M_n(ℝ) | Statement: [GL(n,ℝ), isDenseIn, M_n(ℝ)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isDenseIn Context triple: [GL(n,ℝ), isDenseIn, M_n(ℝ)]
-
A.
isNowhereDenseIn
Indicates that one set is so sparse within another space that its closure has empty interior, meaning it does not contain any nontrivial open subset of that space.
-
B.
isNowhereDense
Indicates that a set is so sparse in the space that the closure of the set has empty interior, meaning it contains no nontrivial open subset.
-
C.
isClosedAndNowhereDense
Indicates that a set is both closed (contains all its limit points) and nowhere dense (its closure has empty interior, so it is "small" in the topological sense).
-
D.
isLatticeIn
Indicates that one structure forms a lattice within, or relative to, another structure or context.
-
E.
hasBasisIn
Indicates that one entity is founded, derived, or justified on the grounds of another entity.
- 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_69e25d2593c88190bcdf4a716a94ccb2 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a3af45ec8190a32aa4e5f04f6756 |
completed | April 29, 2026, 6:22 a.m. |
| PD | Predicate disambiguation | batch_69f061c7aaa48190a58ce93f87155ffc |
completed | April 28, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f0bd4a0e408190ad8916faf23562d9 |
completed | April 28, 2026, 1:59 p.m. |
Created at: April 17, 2026, 5:32 p.m.