Triple
T197654
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deep Learning (book) |
E4032
|
entity |
| Predicate | usesNotation |
P6184
|
FINISHED |
| Object | mathematical notation |
—
|
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: mathematical notation | Statement: [Deep Learning (book), usesNotation, mathematical notation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesNotation Context triple: [Deep Learning (book), usesNotation, mathematical notation]
-
A.
distinguishingNotation
Indicates that one entity uses a specific notation or symbol to distinguish or differentiate another entity from similar ones.
-
B.
notationType
Indicates the specific system or style of notation used to represent or encode something (such as music, math, or language).
-
C.
notation
chosen
Indicates a conventional way of symbolically representing or writing something, such as concepts, quantities, or operations, within a specific system.
-
D.
typicalNotation
Indicates that one entity is the standard or commonly used symbolic representation (notation) for another entity.
-
E.
doesNotUse
Indicates that one entity intentionally refrains from employing, utilizing, or relying on another entity, method, or resource.
- 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_69a254bca59881909a15e1496f1508c7 |
completed | Feb. 28, 2026, 2:36 a.m. |
| NER | Named-entity recognition | batch_69a25be47ea881909c296b30a0d47a65 |
completed | Feb. 28, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69a25b47481c8190add47c641c977bb9 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:44 a.m.