Triple
T32794371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ferry laws on primary education |
E838716
|
entity |
| Predicate | diminishedUseOf |
P89202
|
FINISHED |
| Object | regional languages in schools |
—
|
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: regional languages in schools | Statement: [Ferry laws on primary education, diminishedUseOf, regional languages in schools]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: diminishedUseOf Context triple: [Ferry laws on primary education, diminishedUseOf, regional languages in schools]
-
A.
isReducedDuringUseTo
Indicates that the quantity, intensity, or effectiveness of one entity decreases as a direct result of being used or consumed.
-
B.
usedLessIn
chosen
Indicates that one entity is used with a lower frequency or intensity compared to another entity.
-
C.
decreasesWhen
Indicates that the value or intensity of one entity becomes lower as a result of changes in another entity or condition.
-
D.
reduces
Indicates that one entity causes a decrease in the amount, intensity, degree, or impact of another entity.
-
E.
declineIn
Indicates a relationship where one entity experiences a decrease or reduction in the level, amount, or intensity of another entity over time.
- 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_69f3493c7f6881908edf2aa13631d1e0 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6ce6d659881909ddcec1d2966e020 |
completed | May 3, 2026, 4:26 a.m. |
| PD | Predicate disambiguation | batch_69f6cc1667a48190b42684f6ec22dae9 |
completed | May 3, 2026, 4:16 a.m. |
Created at: May 1, 2026, 1:14 a.m.