Triple
T36151456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | pairing heap |
E1045596
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | self-adjusting heap |
C33132
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: self-adjusting heap Context triple: [pairing heap, instanceOf, self-adjusting heap]
-
A.
self-balancing search tree
A self-balancing search tree is a binary search tree that automatically adjusts its structure during insertions and deletions to maintain near-optimal height for efficient search, insertion, and deletion operations.
-
B.
priority queue implementation
chosen
A priority queue implementation is a data structure that stores elements with associated priorities and allows efficient retrieval and removal of the element with the highest (or lowest) priority.
-
C.
adaptive suspension system
An adaptive suspension system is a vehicle suspension technology that continuously adjusts damping and stiffness in real time based on driving conditions, road surface, and driver inputs to optimize comfort, handling, and stability.
-
D.
active queue management algorithm
An active queue management algorithm is a network mechanism that proactively controls packet queues by selectively dropping or marking packets before buffers overflow to reduce congestion, latency, and packet loss.
-
E.
garbage collection system
A garbage collection system is a mechanism that automatically identifies and reclaims memory or resources that are no longer in use, preventing leaks and optimizing overall system performance.
- F. None of above.
Provenance (1 batch)
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_69f76e37ace88190a906b107d388f5d1 |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:08 p.m.