Triple
T14860105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Deutsch–Jozsa algorithm |
E349464
|
entity |
| Predicate | numberOfOracleQueriesClassicalDeterministicWorstCase |
P29380
|
FINISHED |
| Object | 2^(n-1)+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: 2^(n-1)+1 | Statement: [Deutsch–Jozsa algorithm, numberOfOracleQueriesClassicalDeterministicWorstCase, 2^(n-1)+1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfOracleQueriesClassicalDeterministicWorstCase Context triple: [Deutsch–Jozsa algorithm, numberOfOracleQueriesClassicalDeterministicWorstCase, 2^(n-1)+1]
-
A.
numberOfQueries
chosen
Indicates the total count of queries associated with or performed in a given context or entity.
-
B.
complexityClassRelation
Indicates a relationship between two computational complexity classes, such as inclusion, equivalence, or separation, within the hierarchy of complexity theory.
-
C.
numberOfPredications
Indicates the total count of individual predication instances or assertions associated with a given subject or context.
-
D.
numberOfInstances
Indicates the quantity or count of distinct occurrences or instances associated with a given entity or context.
-
E.
numberOfTruths
Indicates the quantity of statements or propositions that are true within a given context or set.
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded44598e48190b759a05ed2d9ecaf |
completed | April 14, 2026, 11:56 p.m. |
| PD | Predicate disambiguation | batch_69de8c1798c08190b433e9ad21e41a42 |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:54 a.m.