Triple
T1056952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Euclidean space |
E22816
|
entity |
| Predicate | hasFieldOfScalars |
P22981
|
FINISHED |
| Object | real numbers |
—
|
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: real numbers | Statement: [Euclidean space, hasFieldOfScalars, real numbers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFieldOfScalars Context triple: [Euclidean space, hasFieldOfScalars, real numbers]
-
A.
hasVector
Indicates that an entity is associated with, or can be represented by, a specific vector in some vector space.
-
B.
hasCollectionType
Indicates that an entity is associated with or organized under a specific type or category of collection.
-
C.
fieldPresenceIn
Indicates that something exists or is located within a particular field, area, or domain.
-
D.
hasDimension
Indicates that an entity possesses a specific measurable extent or size along one or more axes (e.g., length, width, height).
-
E.
hasFieldColor
Indicates that an entity possesses a field whose color is specified by another entity or value.
- 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_69a493dada0481909c43649f9843ea91 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8da80dc8190b79beaf509910725 |
completed | March 1, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69a4b731e25c8190b5ea8466648c2c9a |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b7da38888190a118ef20ce4ae9aa |
completed | March 1, 2026, 10:04 p.m. |
Created at: March 1, 2026, 7:42 p.m.