Triple
T33320269
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stiefel–Whitney classes |
E853116
|
entity |
| Predicate | totalClassNotation |
P4882
|
FINISHED |
| Object | w(E) = 1 + w_1(E) + w_2(E) + ⋯ |
—
|
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: w(E) = 1 + w_1(E) + w_2(E) + ⋯ | Statement: [Stiefel–Whitney classes, totalClassNotation, w(E) = 1 + w_1(E) + w_2(E) + ⋯]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalClassNotation Context triple: [Stiefel–Whitney classes, totalClassNotation, w(E) = 1 + w_1(E) + w_2(E) + ⋯]
-
A.
typicalNotation
chosen
Indicates that one entity is the standard or commonly used symbolic representation (notation) for another entity.
-
B.
distinguishingNotation
Indicates that one entity uses a specific notation or symbol to distinguish or differentiate another entity from similar ones.
-
C.
formalNotation
Indicates that one entity is the formal symbolic or notational representation of another entity or concept.
-
D.
lengthClass
Indicates a classification relationship where an entity is assigned to a category based on its length.
-
E.
notableClass
Indicates that an entity belongs to a particularly important, distinguished, or otherwise noteworthy class or category within a given classification system.
- 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_69f349685f088190b8fda44083a018a9 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f79f48acec8190a9d5964581a94f6c |
completed | May 3, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f79e4888248190be2f63cdfb5cd7b7 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 1, 2026, 1:33 a.m.