Triple
T5245527
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blum–Blum–Shub pseudorandom number generator |
E118448
|
entity |
| Predicate | relatedToProblem |
P37
|
FINISHED |
| Object | integer factorization problem |
—
|
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: integer factorization problem | Statement: [Blum–Blum–Shub pseudorandom number generator, relatedToProblem, integer factorization problem]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedToProblem Context triple: [Blum–Blum–Shub pseudorandom number generator, relatedToProblem, integer factorization problem]
-
A.
relatedTo
chosen
Indicates a general, non-specific relationship or association exists between two entities.
-
B.
relatedToProject
Indicates that an entity has a connection or association with a specific project, without specifying the exact nature of that involvement.
-
C.
relatedQuestion
Indicates that one question is connected to another by topic, context, or relevance, suggesting they are meaningfully associated.
-
D.
relatedTest
Indicates that there exists some form of connection or association between one test and another.
-
E.
relatedType
Indicates that one entity is connected to another through a specified type or category of relationship.
- 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_69bd4468aacc8190a8196f71855cdf4f |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd7b515edc8190a9db198d4eb1c4f6 |
completed | March 20, 2026, 4:52 p.m. |
| PD | Predicate disambiguation | batch_69bd77c1397c8190a7fd844d7a396e54 |
completed | March 20, 2026, 4:37 p.m. |
Created at: March 20, 2026, 1:49 p.m.