Triple
T22964750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sylvester sequence |
E571006
|
entity |
| Predicate | alternativeRecurrence |
P150422
|
FINISHED |
| Object | a_{n+1} = a_n^2 - a_n + 1 for n ≥ 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: a_{n+1} = a_n^2 - a_n + 1 for n ≥ 1 | Statement: [Sylvester sequence, alternativeRecurrence, a_{n+1} = a_n^2 - a_n + 1 for n ≥ 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: alternativeRecurrence
Context triple: [Sylvester sequence, alternativeRecurrence, a_{n+1} = a_n^2 - a_n + 1 for n ≥ 1]
-
A.
recurrenceType
Indicates the pattern or frequency with which an event or action repeats over time.
-
B.
recurringEvent
Indicates that an event occurs repeatedly over time according to some regular pattern or schedule.
-
C.
recurringSeries
Indicates that an event, action, or pattern occurs repeatedly over time as part of an ongoing series rather than as a one-time instance.
-
D.
recurrence
Indicates that an event, condition, or state happens again or repeatedly over time, often after a period of absence or resolution.
-
E.
alternativeCounting
Indicates that there exists another valid way of counting or enumerating the same set of items or events, distinct from the primary counting method.
- 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_69e245b212a88190b5259caf51606084 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f181f763688190aab8f444a1a71577 |
completed | April 29, 2026, 3:58 a.m. |
| PD | Predicate disambiguation | batch_69ef3b9101f48190a06c69dff26c6441 |
completed | April 27, 2026, 10:33 a.m. |
| PDg | Predicate description generation | batch_69ef538a115081908982597f79355840 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 17, 2026, 3:47 p.m.