Triple
T1324381
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramadan |
E28290
|
entity |
| Predicate | effectOnSchedule |
P26550
|
FINISHED |
| Object | changes in work and school hours in some Muslim-majority countries |
—
|
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: changes in work and school hours in some Muslim-majority countries | Statement: [Ramadan, effectOnSchedule, changes in work and school hours in some Muslim-majority countries]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnSchedule Context triple: [Ramadan, effectOnSchedule, changes in work and school hours in some Muslim-majority countries]
-
A.
effectOnCampaign
Indicates the influence or impact that one factor has on the outcome or performance of a campaign.
-
B.
schedule
Indicates that an entity arranges for an event, task, or activity to occur at a specific time or within a defined time frame.
-
C.
effectDuration
Indicates the length of time for which an effect remains active or valid.
-
D.
notableEffect
Indicates that one entity has a significant impact, consequence, or influence on another entity or situation.
-
E.
actsOn
Indicates that one entity performs an action that affects, targets, or is directed toward another entity.
- 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_69a498540a2481909e807a762280d3ba |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c19e81c0819092f85201ae34422a |
completed | March 1, 2026, 10:45 p.m. |
| PD | Predicate disambiguation | batch_69a4beedb49c8190beb5b85cdda05013 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4bf8158ac8190b8360ecccc2980bc |
completed | March 1, 2026, 10:36 p.m. |
Created at: March 1, 2026, 7:55 p.m.