Triple
T4277267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KMeans |
E97072
|
entity |
| Predicate | optimizationProblem |
P15680
|
FINISHED |
| Object | NP-hard in general |
—
|
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: NP-hard in general | Statement: [KMeans, optimizationProblem, NP-hard in general]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: optimizationProblem Context triple: [KMeans, optimizationProblem, NP-hard in general]
-
A.
optimizationType
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
B.
typeOfOptimality
Indicates that one entity specifies the particular notion or criterion of optimality that characterizes another entity’s optimal status or solution.
-
C.
optimizationTarget
Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
-
D.
optimize
Indicates improving a process, system, or outcome to achieve the best possible performance or efficiency under given constraints.
-
E.
problemStatement
chosen
Indicates that an entity presents, defines, or expresses a specific problem or issue to be addressed.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501ef1388190b0c968b069014a59 |
completed | March 12, 2026, 11:45 p.m. |
| PD | Predicate disambiguation | batch_69b347faa45481908c19c29fb906dc92 |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:07 p.m.