Triple
T14911051
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tukey's biweight |
E371259
|
entity |
| Predicate | objectiveFunctionType |
P116644
|
FINISHED |
| Object | loss function that is quadratic near zero residuals |
—
|
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: loss function that is quadratic near zero residuals | Statement: [Tukey's biweight, objectiveFunctionType, loss function that is quadratic near zero residuals]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: objectiveFunctionType Context triple: [Tukey's biweight, objectiveFunctionType, loss function that is quadratic near zero residuals]
-
A.
typeOfOptimality
Indicates that one entity specifies the particular notion or criterion of optimality that characterizes another entity’s optimal status or solution.
-
B.
optimizationType
Indicates the specific strategy or method used to improve performance or efficiency within a given process or system.
-
C.
performanceFunction
Indicates a relationship where a function or mapping quantifies or evaluates the performance level of an entity, action, or system.
-
D.
trainingObjective
Indicates the goal or target outcome that a training process is designed to achieve.
-
E.
optimizationSolver
Indicates a relationship where a solver entity is used to compute an optimal solution for a given optimization problem or task.
- 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_69d85cc7ea3481908228b5acb7d06f12 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded61c6b9c8190a92934d49b98fe46 |
completed | April 15, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69de9a4a14a88190951bb8f4c60bd37b |
completed | April 14, 2026, 7:49 p.m. |
| PDg | Predicate description generation | batch_69deb1a4d8dc8190a4c0841c20f2875f |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 2:26 a.m.