Triple
T21509889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China–North Korea relations |
E530686
|
entity |
| Predicate | covidPeriodImpact |
P8645
|
FINISHED |
| Object | sharp reduction in official trade |
—
|
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: sharp reduction in official trade | Statement: [China–North Korea relations, covidPeriodImpact, sharp reduction in official trade]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: covidPeriodImpact Context triple: [China–North Korea relations, covidPeriodImpact, sharp reduction in official trade]
-
A.
covid19Impact
chosen
Indicates the effect, consequences, or influence that COVID-19 has on a given entity, condition, or situation.
-
B.
epidemicImpact
Indicates the extent and nature of how an epidemic affects entities, such as populations, regions, or systems.
-
C.
stanceOnCOVID-19Measures
Indicates a subject’s position, attitude, or level of support or opposition toward policies and actions taken in response to COVID-19 (such as restrictions, mandates, or public health measures).
-
D.
experiencedEpidemic
Indicates that an entity has undergone or been affected by an epidemic event.
-
E.
timePeriodOfSchoolClosures
Indicates the span of time during which schools are closed.
- 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_69e0c45c81f08190a6b8bbb70a45aae7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea84dfbc8190a23d9a7d6eb2c2b5 |
completed | April 23, 2026, 9:46 a.m. |
| PD | Predicate disambiguation | batch_69e631f6e68081908f5ee4ce7413803e |
completed | April 20, 2026, 2:02 p.m. |
Created at: April 16, 2026, 6:25 p.m.