Triple
T6991719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thompson's algorithm |
E162099
|
entity |
| Predicate | complexityClass |
P27167
|
FINISHED |
| Object | O(n) in size of regular expression |
—
|
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: O(n) in size of regular expression | Statement: [Thompson's algorithm, complexityClass, O(n) in size of regular expression]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: complexityClass Context triple: [Thompson's algorithm, complexityClass, O(n) in size of regular expression]
-
A.
complexityClassRelation
Indicates a relationship between two computational complexity classes, such as inclusion, equivalence, or separation, within the hierarchy of complexity theory.
-
B.
computationalClass
Indicates that two entities share the same computational complexity class or that one entity is categorized within a specified computational complexity class.
-
C.
timeComplexity
chosen
Indicates the computational growth rate of an algorithm’s resource usage (typically time) as a function of input size.
-
D.
hasComplexity
Indicates that something possesses a certain level or type of complexity, often in terms of structure, behavior, or difficulty.
-
E.
spaceComplexity
Indicates the relationship between an algorithm and the amount of memory it requires as a function of input size.
- 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_69c68856d7808190ab33ee914640281b |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbc1f63c8190837cfd71cf5ed613 |
completed | March 27, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c4a18881908d267137daed828b |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:32 p.m.