Triple
T6971941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | East Asian summer monsoon |
E161617
|
entity |
| Predicate | bringsAirMass |
P26038
|
FINISHED |
| Object | warm moist air from the western North Pacific |
—
|
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: warm moist air from the western North Pacific | Statement: [East Asian summer monsoon, bringsAirMass, warm moist air from the western North Pacific]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bringsAirMass Context triple: [East Asian summer monsoon, bringsAirMass, warm moist air from the western North Pacific]
-
A.
capturesAtmosphereOf
Indicates that one entity successfully conveys or reflects the overall mood, tone, or ambiance characteristic of another entity.
-
B.
hasAtmosphericFeature
chosen
Indicates that one entity possesses or exhibits a particular feature or characteristic of its atmosphere.
-
C.
usesAtmosphereOf
Indicates that one entity makes use of or operates within the atmospheric conditions or composition associated with another entity.
-
D.
landingMass
Indicates the mass of an object or vehicle at the moment it lands.
-
E.
hasAtmosphere
Indicates that an astronomical body possesses a surrounding layer of gases held by its gravity.
- 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_69c68854a0d88190bc0bf82263f1afce |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db398f10819096d34b179ccb20d5 |
completed | March 27, 2026, 7:32 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c262508190a7708b3d9cf23d7c |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:30 p.m.