Triple
T23036177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pareto improvement |
E573600
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | Pareto optimality |
—
|
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 optimality | Statement: [Pareto improvement, relatedTo, Pareto optimality]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pareto optimality Context triple: [Pareto improvement, relatedTo, Pareto optimality]
-
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 improvement
A Pareto improvement is a change in allocation that makes at least one individual better off without making anyone else worse off.
-
C.
Nash equilibrium
A Nash equilibrium is a game-theoretic solution concept where no player can improve their payoff by unilaterally changing their strategy, given the strategies of all other players.
-
D.
fundamental theorems of welfare economics
The fundamental theorems of welfare economics are core results in microeconomic theory that formally link competitive market equilibria with Pareto efficiency and the conditions under which any efficient allocation can be supported as a market equilibrium.
-
E.
Karush–Kuhn–Tucker conditions
The Karush–Kuhn–Tucker conditions are fundamental optimality criteria in nonlinear programming that generalize Lagrange multipliers to handle inequality constraints.
- 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.