Triple
T31130751
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Longyang Road |
E793496
|
entity |
| Predicate | hasAssociatedMetroStation |
P39430
|
FINISHED |
| Object | Longyang Road station |
—
|
NE NERFINISHED |
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: Longyang Road station | Statement: [Longyang Road, hasAssociatedMetroStation, Longyang Road station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAssociatedMetroStation Context triple: [Longyang Road, hasAssociatedMetroStation, Longyang Road station]
-
A.
hasMetroTerminus
Indicates that one location serves as the terminal (end) station of a metro line for another location.
-
B.
hasMetroStationArea
Indicates that a specified area contains or is served by a metro (subway) station.
-
C.
hasMetroStations
chosen
Indicates that a place or area is served by one or more metro (subway) stations.
-
D.
hasMetroLine
Indicates that a location or area is served by, or connected to, a specific metro (subway) line.
-
E.
hasMetropolitanConnectionWith
Indicates that there is a significant relationship or linkage between two entities based on shared or interacting metropolitan areas, such as through infrastructure, services, or regional integration.
- 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_69f224d1701c819094f429798290e361 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
Created at: April 29, 2026, 9:05 p.m.