Triple
T27562855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legendre polynomials |
E695818
|
entity |
| Predicate | haveNormalization |
P162707
|
FINISHED |
| Object | P_n(1) = 1 |
—
|
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: P_n(1) = 1 | Statement: [Legendre polynomials, haveNormalization, P_n(1) = 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: haveNormalization Context triple: [Legendre polynomials, haveNormalization, P_n(1) = 1]
-
A.
usesNormalization
Indicates that one entity applies or relies on a normalization process or technique in relation to another entity or data.
-
B.
hasNorm
Indicates that an entity is associated with, governed by, or characterized through a particular norm, rule, or standard.
-
C.
normalizationInvolves
Indicates that a normalization process includes or makes use of a particular component, step, or element as part of its execution.
-
D.
refinesNormalization
Indicates that one normalization process or scheme improves, clarifies, or makes more precise another existing normalization.
-
E.
normalizationType
Indicates the specific method or scheme used to normalize or standardize data, values, or representations within a given context.
- 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_69ef53891af88190a193c5e2a1dac9b1 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f62fbbc4408190b9afd456a429f61b |
completed | May 2, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69f62c1921008190a62675a31f66a875 |
completed | May 2, 2026, 4:53 p.m. |
| PDg | Predicate description generation | batch_69f62d14c24c81909e86678c1b5fd429 |
completed | May 2, 2026, 4:57 p.m. |
Created at: April 27, 2026, 1:40 p.m.