Triple
T15736615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Khinchin–Lévy constant |
E381487
|
entity |
| Predicate | approximateSquare |
P120413
|
FINISHED |
| Object | 10.7350 |
—
|
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: 10.7350 | Statement: [Khinchin–Lévy constant, approximateSquare, 10.7350]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: approximateSquare Context triple: [Khinchin–Lévy constant, approximateSquare, 10.7350]
-
A.
approximateRadius
Indicates that one entity specifies or provides an estimated value for the radius of another entity.
-
B.
squareUse
Indicates that one entity uses or occupies another entity in a square or rectangular configuration, typically implying a grid-like or evenly spaced arrangement.
-
C.
approximates
Indicates that one entity is close to, but not exactly equal to, the value, form, or behavior of another entity.
-
D.
approximateSideLengthOrderOfMagnitude
Indicates that one entity’s side length is approximately the same order of magnitude as another entity’s side length, differing by no more than about a factor of ten.
-
E.
approximateDiameter
Indicates that one entity specifies the estimated or rough measurement of another entity’s diameter.
- 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_69d86d9cdb648190bf3171be0bd7d872 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d6b5788190883746ee82c799f5 |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e0052c6208819098165d61d378d13b |
completed | April 15, 2026, 9:37 p.m. |
| PDg | Predicate description generation | batch_69e0b4d01c9c81909f6b611e8144c838 |
completed | April 16, 2026, 10:07 a.m. |
Created at: April 10, 2026, 4:46 a.m.