Triple
T23036213
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pareto improvement |
E573600
|
entity |
| Predicate | relatedConcept |
P37
|
FINISHED |
| Object | Pareto frontier |
—
|
NE NERFINISHED |
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: Pareto frontier | Statement: [Pareto improvement, relatedConcept, Pareto frontier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pareto frontier Context triple: [Pareto improvement, relatedConcept, Pareto frontier]
-
A.
Pareto efficiency
chosen
Pareto efficiency is an economic concept describing an allocation of resources where no individual can be made better off without making someone else worse off.
-
B.
Pareto
Pareto is an Italian surname most famously associated with economist and sociologist Vilfredo Pareto, whose work led to the Pareto principle (the 80/20 rule).
-
C.
Electre
Electre is a modern French theatrical adaptation of the Electra myth written by playwright Jean Giraudoux.
-
D.
Karush–Kuhn–Tucker conditions
The Karush–Kuhn–Tucker conditions are fundamental optimality criteria in nonlinear programming that generalize Lagrange multipliers to handle inequality constraints.
-
E.
MOEA
MOEA is Taiwan’s central government ministry responsible for formulating and implementing national economic, industrial, trade, and energy policies.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245b911188190bc3d96326c847969 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1850fe5348190b42259595d82cff4 |
completed | April 29, 2026, 4:12 a.m. |
Created at: April 17, 2026, 3:53 p.m.