Triple
T25905767
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Economy of Paraguay |
E652745
|
entity |
| Predicate | inequalityIndicator |
P23539
|
FINISHED |
| Object | high Gini coefficient |
—
|
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: high Gini coefficient | Statement: [Economy of Paraguay, inequalityIndicator, high Gini coefficient]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inequalityIndicator Context triple: [Economy of Paraguay, inequalityIndicator, high Gini coefficient]
-
A.
dataProcessingInequality
Indicates that processing data through a (possibly noisy) transformation cannot increase the information one variable has about another, so the mutual information between them can only stay the same or decrease.
-
B.
incomeMeasure
Indicates a relationship where one entity serves as a measure or metric of another entity’s income.
-
C.
statesInequality
Indicates that one entity asserts or expresses that two values or quantities are not equal or that an inequality relation holds between them.
-
D.
indicatorType
chosen
Indicates the specific kind or category of indicator associated with an entity or relationship.
-
E.
economicStatus
Indicates the financial or socioeconomic condition or standing of an entity relative to others or to defined economic criteria.
- 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_69e7ab3d3f8481909bc53ed64c06af33 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f603beb5248190aed52bf4e44f223c |
completed | May 2, 2026, 2:01 p.m. |
| PD | Predicate disambiguation | batch_69f4a0fed15881909b789251fe5d8d45 |
completed | May 1, 2026, 12:47 p.m. |
Created at: April 22, 2026, 8:27 a.m.