Triple
T685833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beijing Daxing International Airport |
E13280
|
entity |
| Predicate | terminalArea |
P18217
|
FINISHED |
| Object | about 700000 square meters |
—
|
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: about 700000 square meters | Statement: [Beijing Daxing International Airport, terminalArea, about 700000 square meters]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: terminalArea Context triple: [Beijing Daxing International Airport, terminalArea, about 700000 square meters]
-
A.
terminal
Indicates that one entity is the final or end point in a process, sequence, or structure, beyond which no further continuation occurs.
-
B.
targetArea
Indicates the specific area or region that is the intended focus or destination of an action or effect.
-
C.
areaComponent
Indicates that one area is a constituent or sub-area that forms part of a larger area.
-
D.
navigationArea
Indicates that a specified region or space is designated for movement, routing, or pathfinding within an environment.
-
E.
terminusEast
Indicates that one entity serves as the eastern endpoint or final stop of another entity, such as a route, line, or path.
- 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_69a4933e0f98819097d22766c49b61b8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0f55f7481909e052a25bd12d455 |
completed | March 1, 2026, 8:26 p.m. |
| PD | Predicate disambiguation | batch_69a49d2048d48190ab99ab59accb6909 |
completed | March 1, 2026, 8:10 p.m. |
| PDg | Predicate description generation | batch_69a4a0f405748190ba72a9cfe946a8ec |
completed | March 1, 2026, 8:26 p.m. |
Created at: March 1, 2026, 7:36 p.m.