Triple
T7399566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tianjin South Railway Station |
E170709
|
entity |
| Predicate | hasWaitingRoomForDisabledPassengers |
P3382
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Tianjin South Railway Station, hasWaitingRoomForDisabledPassengers, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWaitingRoomForDisabledPassengers Context triple: [Tianjin South Railway Station, hasWaitingRoomForDisabledPassengers, yes]
-
A.
hasSeatStatus
Indicates the current condition or availability state of a seat in a given context.
-
B.
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.
-
C.
hasOccupancyStatus
Indicates the current usage or availability state of something, such as whether it is occupied, vacant, or otherwise in use.
-
D.
hasPassengerRole
Indicates that an entity participates in a context or event specifically in the capacity or role of a passenger.
-
E.
hasPassengerOperations
Indicates that an entity conducts or supports transportation services specifically for carrying passengers.
- 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_69c68a5f04188190ac266569c9280347 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f24dbf288190b8dfea455148841b |
completed | March 27, 2026, 9:10 p.m. |
| PD | Predicate disambiguation | batch_69c6f0323b2c819098ab72c33e6d8534 |
completed | March 27, 2026, 9:01 p.m. |
Created at: March 27, 2026, 3:10 p.m.