Triple
T4092236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lebesgue spaces |
E87728
|
entity |
| Predicate | L2InnerProduct |
P52975
|
FINISHED |
| Object | ∫ f·conjugate(g) dμ |
—
|
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: ∫ f·conjugate(g) dμ | Statement: [Lebesgue spaces, L2InnerProduct, ∫ f·conjugate(g) dμ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: L2InnerProduct Context triple: [Lebesgue spaces, L2InnerProduct, ∫ f·conjugate(g) dμ]
-
A.
usedAsL2By
Indicates that something serves as a second language (L2) for a particular user or group of users.
-
B.
isUsedToCompute
Indicates that one entity serves as an input, basis, or resource for performing a calculation or deriving a result about another entity.
-
C.
usesLossFunction
Indicates that one entity employs a particular loss function as part of its optimization or learning process.
-
D.
trainingCompute
Indicates the amount or configuration of computational resources used to train a model or system.
-
E.
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).
- 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_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. |
| PDg | Predicate description generation | batch_69aef9b34dec81909bbc3def9decc71a |
completed | March 9, 2026, 4:47 p.m. |
Created at: March 9, 2026, 3:40 p.m.