Triple
T26255113
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farey sequence |
E656694
|
entity |
| Predicate | cardinalityFormula |
P23694
|
FINISHED |
| Object | |F_n| = 1 + sum_{m=1}^n φ(m) |
—
|
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_n| = 1 + sum_{m=1}^n φ(m) | Statement: [Farey sequence, cardinalityFormula, |F_n| = 1 + sum_{m=1}^n φ(m)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cardinalityFormula
Context triple: [Farey sequence, cardinalityFormula, |F_n| = 1 + sum_{m=1}^n φ(m)]
-
A.
cardinality
chosen
Indicates the number of distinct elements or members in a given set or collection.
-
B.
cardinalityCondition
Indicates a constraint on the number of instances or occurrences that a related entity or relationship must or may have.
-
C.
cardinalityExample
Indicates a relationship where an example is provided to illustrate the number or quantity (cardinality) of related entities in a given context.
-
D.
createdCardinalOf
Indicates that an entity established or founded the position or office of a cardinal for another entity.
-
E.
cardinalityOfFibers
Indicates the number of distinct fibers (preimage sets) associated with a given mapping or relation.
- 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_69ee5b4d25ac819086acb51184602576 |
completed | April 26, 2026, 6:37 p.m. |
| NER | Named-entity recognition | batch_69f60dcd2e688190b54e36b0ff0d9187 |
completed | May 2, 2026, 2:44 p.m. |
| PD | Predicate disambiguation | batch_69f5f7ff548c8190a23e98c5e66e0bc7 |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 9:08 p.m.