Triple
T7399558
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tianjin South Railway Station |
E170709
|
entity |
| Predicate | hasWaitingAreaClass |
P3382
|
FINISHED |
| Object | first-class waiting area |
—
|
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: first-class waiting area | Statement: [Tianjin South Railway Station, hasWaitingAreaClass, first-class waiting area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWaitingAreaClass Context triple: [Tianjin South Railway Station, hasWaitingAreaClass, first-class waiting area]
-
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.
hasReservationArea
Indicates that an entity is assigned or associated with a specific reserved area or section designated for its use.
-
C.
hasWorkArea
Indicates that one entity possesses, is assigned, or is associated with a specific physical or conceptual work area.
-
D.
hasArrivalArea
Indicates that an entity is associated with a specific area designated for arrivals, such as where incoming people or items first enter or are received.
-
E.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
- 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.