Triple
T461815
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | de Sitter spacetime |
E7354
|
entity |
| Predicate | hasEmbeddingSpace |
P5376
|
FINISHED |
| Object | 5-dimensional Minkowski space |
—
|
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: 5-dimensional Minkowski space | Statement: [de Sitter spacetime, hasEmbeddingSpace, 5-dimensional Minkowski space]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmbeddingSpace Context triple: [de Sitter spacetime, hasEmbeddingSpace, 5-dimensional Minkowski space]
-
A.
embeddingType
Indicates the specific kind or category of embedding representation used to encode an entity or data.
-
B.
hasLanguageModel
Indicates that an entity possesses, uses, or is associated with a particular language model.
-
C.
hasVector
Indicates that an entity is associated with, or can be represented by, a specific vector in some vector space.
-
D.
canBeEmbeddedIn
chosen
Indicates that one entity can be inserted or integrated within another entity, typically preserving structure or compatibility.
-
E.
embodiedBy
Indicates that an abstract concept, role, or function is physically or concretely realized in a specific entity.
- F. None of above.
Provenance (3 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_69a2e7e5c5bc8190a1dc8178218fba40 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2efbed5b88190a45716812eb4cfdf |
completed | Feb. 28, 2026, 1:38 p.m. |
| PD | Predicate disambiguation | batch_69a2ede8eac081908dffade6a5e7950b |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.