Triple
T4092239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lebesgue spaces |
E87728
|
entity |
| Predicate | L1Dual |
P31338
|
FINISHED |
| Object | L^∞ in many standard measure spaces |
—
|
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: L^∞ in many standard measure spaces | Statement: [Lebesgue spaces, L1Dual, L^∞ in many standard measure spaces]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: L1Dual Context triple: [Lebesgue spaces, L1Dual, L^∞ in many standard measure spaces]
-
A.
regularization
Indicates the application of a constraint or penalty to a model or function to prevent overfitting and encourage simpler, more generalizable behavior.
-
B.
dualPair
chosen
Indicates that two entities form a dual pair, standing in a mathematically defined dual relationship where each is the dual counterpart of the other.
-
C.
normType
Indicates the specific category or classification of a norm that governs or constrains an entity or situation.
-
D.
isLinear
Indicates that a relationship, function, or structure preserves linearity, typically meaning it satisfies additivity and homogeneity (or forms a straight-line dependence between variables).
-
E.
slopeUse
Indicates how a particular slope or gradient is utilized or purposed in relation to another 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_69aed94425148190be337845d56fac22 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefcae22a081908af65a960306b78c |
completed | March 9, 2026, 5 p.m. |
| PD | Predicate disambiguation | batch_69aef909c9c88190b09d48dad325a83c |
completed | March 9, 2026, 4:44 p.m. |
Created at: March 9, 2026, 3:40 p.m.