Triple
T18791220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Insertion sort |
E459517
|
entity |
| Predicate | numberOfShiftsWorstCase |
P133030
|
FINISHED |
| Object | O(n^2) |
—
|
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^2) | Statement: [Insertion sort, numberOfShiftsWorstCase, O(n^2)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfShiftsWorstCase Context triple: [Insertion sort, numberOfShiftsWorstCase, O(n^2)]
-
A.
hasNumberOfShifts
Indicates the quantity of work shifts associated with a given entity.
-
B.
shiftsWhen
Indicates that one state, condition, or configuration changes to another under specified circumstances or triggers.
-
C.
hasDynamicShifts
Indicates that something exhibits changes or transitions in state, intensity, or behavior over time rather than remaining constant.
-
D.
numberOfEmployeesAtPeak
Indicates the highest recorded count of employees that an entity had at any point in time.
-
E.
overtimePeriodCount
Indicates the number of overtime periods that occurred or are allocated in a given event or context.
- F. None of above. chosen
Provenance (4 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_69d8d396f54c8190ba49db31e8743842 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5978665388190aaefed0ec30a1ff3 |
completed | April 20, 2026, 3:03 a.m. |
| PD | Predicate disambiguation | batch_69e48d16dd34819096e096d0c0e4c15c |
completed | April 19, 2026, 8:06 a.m. |
| PDg | Predicate description generation | batch_69e49785fd7081909577e90a55df0a35 |
completed | April 19, 2026, 8:51 a.m. |
Created at: April 10, 2026, 11:53 a.m.