Triple
T21494364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carmichael number |
E530314
|
entity |
| Predicate | factorizationExample |
P27191
|
FINISHED |
| Object | 561 = 3 × 11 × 17 |
—
|
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: 561 = 3 × 11 × 17 | Statement: [Carmichael number, factorizationExample, 561 = 3 × 11 × 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: factorizationExample Context triple: [Carmichael number, factorizationExample, 561 = 3 × 11 × 17]
-
A.
primeFactorization
chosen
Indicates that one entity is the decomposition of another entity into a multiset or sequence of prime factors whose product equals the original.
-
B.
factor
Indicates that one entity is a contributing cause, influence, or component affecting the state, outcome, or existence of another entity.
-
C.
divisionExample
Indicates an example that illustrates or demonstrates a particular division or partitioning of something.
-
D.
yieldsDecomposition
Indicates that one entity produces or results in a particular breakdown or decomposition of another entity.
-
E.
simplifiedForm
Indicates that one entity is a reduced or less complex version of another, preserving essential meaning while omitting detail or complexity.
- 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_69e0c45bd15481909fba5910765cdda2 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea567244819091863350fedae3ae |
completed | April 23, 2026, 9:45 a.m. |
| PD | Predicate disambiguation | batch_69e631f6e68081908f5ee4ce7413803e |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:23 p.m.