Triple
T36151367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kruskal’s minimum spanning tree algorithm |
E1045593
|
entity |
| Predicate | optimalFor |
P170381
|
FINISHED |
| Object | sparse graphs |
—
|
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: sparse graphs | Statement: [Kruskal’s minimum spanning tree algorithm, optimalFor, sparse graphs]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimalFor Context triple: [Kruskal’s minimum spanning tree algorithm, optimalFor, sparse graphs]
-
A.
isSuitableFor
Indicates that one entity is appropriate, fitting, or well-matched for use, application, or association with another entity.
-
B.
isPerfectFor
Indicates that one entity is ideally suited or optimally appropriate for another entity, purpose, or context.
-
C.
lessOptimizedFor
Indicates that one entity is designed, configured, or adapted to perform a task or function with lower efficiency or effectiveness compared to another entity.
-
D.
worksBestWhen
chosen
Indicates that one entity performs optimally or is most effective under the conditions provided by another entity.
-
E.
holdsBestFor
Indicates that one entity considers or maintains another entity as the most suitable or optimal choice for a particular purpose or context.
- 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_69f76e37ace88190a906b107d388f5d1 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69f7b3e2f3c08190be4fd1ae4fa1266d |
completed | May 3, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bcc47081909fe7d592ac69006c |
completed | May 3, 2026, 8:36 p.m. |
Created at: May 3, 2026, 4:08 p.m.