Triple
T27562817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legendre polynomials |
E695818
|
entity |
| Predicate | satisfyRecurrence |
P25791
|
FINISHED |
| Object | (n+1)P_{n+1}(x) = (2n+1)xP_n(x) - nP_{n-1}(x) |
—
|
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: (n+1)P_{n+1}(x) = (2n+1)xP_n(x) - nP_{n-1}(x) | Statement: [Legendre polynomials, satisfyRecurrence, (n+1)P_{n+1}(x) = (2n+1)xP_n(x) - nP_{n-1}(x)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: satisfyRecurrence
Context triple: [Legendre polynomials, satisfyRecurrence, (n+1)P_{n+1}(x) = (2n+1)xP_n(x) - nP_{n-1}(x)]
-
A.
satisfiesRecurrence
chosen
Indicates that one entity fulfills or conforms to a specified recurrence relation defined by another entity.
-
B.
definesRecurrenceFor
Indicates that one entity specifies or establishes the recurrence pattern or schedule that applies to another entity.
-
C.
mayRecurAfter
Indicates that an event, condition, or state has the potential to occur again following a previous occurrence.
-
D.
recurringDuring
Indicates that an event or state happens repeatedly within the time span or context defined by another event or interval.
-
E.
recurrenceType
Indicates the pattern or frequency with which an event or action repeats over time.
- 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_69ef53891af88190a193c5e2a1dac9b1 |
completed | April 27, 2026, 12:16 p.m. |
| NER | Named-entity recognition | batch_69f6359e3d3c81909814e2f0a7fb0ea9 |
completed | May 2, 2026, 5:34 p.m. |
| PD | Predicate disambiguation | batch_69f631871c888190bf29466fe4254e51 |
completed | May 2, 2026, 5:16 p.m. |
Created at: April 27, 2026, 1:40 p.m.