Triple
T4277266
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KMeans |
E97072
|
entity |
| Predicate | objectiveFunction |
P33716
|
FINISHED |
| Object | minimize sum of squared distances between points and their assigned cluster centroid |
—
|
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: minimize sum of squared distances between points and their assigned cluster centroid | Statement: [KMeans, objectiveFunction, minimize sum of squared distances between points and their assigned cluster centroid]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: objectiveFunction Context triple: [KMeans, objectiveFunction, minimize sum of squared distances between points and their assigned cluster centroid]
-
A.
trainingObjective
Indicates the goal or target outcome that a training process is designed to achieve.
-
B.
axisObjective
Indicates that an entity serves as a primary goal, target, or focal objective along a defined axis or strategic direction in a given context.
-
C.
evaluationFunction
Indicates a relationship where a specific procedure or rule assigns a value or score to an input, typically to assess its quality, utility, or desirability.
-
D.
optimizationTarget
chosen
Indicates that one entity is the goal or objective that another entity is trying to improve, optimize, or make more efficient.
-
E.
typeOfOptimality
Indicates that one entity specifies the particular notion or criterion of optimality that characterizes another entity’s optimal status or solution.
- 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.