Triple
T12635709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beijing West railway station |
E301757
|
entity |
| Predicate | hasWaitingRoomArea |
P3382
|
FINISHED |
| Object | 80000 square metres |
—
|
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: 80000 square metres | Statement: [Beijing West railway station, hasWaitingRoomArea, 80000 square metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWaitingRoomArea Context triple: [Beijing West railway station, hasWaitingRoomArea, 80000 square metres]
-
A.
hasWaitingArea
chosen
Indicates that an entity provides or includes a designated space where people can wait before receiving a service or proceeding to another area.
-
B.
hasRoom
Indicates that an entity possesses, contains, or is associated with a specific room.
-
C.
hasReservationArea
Indicates that an entity is assigned or associated with a specific reserved area or section designated for its use.
-
D.
hasSpectatorArea
Indicates that a location or facility includes a designated area intended for spectators to observe an event or activity.
-
E.
hasStandingArea
Indicates that an entity includes or provides a designated area where people can stand.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ae493481908f82e0d05dce20bd |
completed | April 10, 2026, 8:46 p.m. |
| PD | Predicate disambiguation | batch_69d960b47130819097e1162ed4fc993a |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:16 p.m.