Triple
T3207669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beijing Railway Station |
E67202
|
entity |
| Predicate | servesRouteType |
P13416
|
FINISHED |
| Object | conventional rail |
—
|
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: conventional rail | Statement: [Beijing Railway Station, servesRouteType, conventional rail]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servesRouteType Context triple: [Beijing Railway Station, servesRouteType, conventional rail]
-
A.
servesRoute
Indicates that a service, vehicle, or provider operates along or provides transportation for a specified route.
-
B.
servesDestinationType
Indicates that an entity provides service to, or is intended for use with, a specific type or category of destination.
-
C.
servesMode
Indicates that one entity provides or operates in a particular manner, method, or mode in relation to another entity or context.
-
D.
hasRouteType
Indicates that there is a specific kind or category of route associated with an entity (e.g., road, rail, bus line).
-
E.
includesRouteType
chosen
Indicates that one entity’s set of routes contains or covers a specific type or category of route associated with another entity.
- 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_69ad858ac36c81909962589cd277d6e2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaa58340881908347d772cfa0ac4c |
completed | March 8, 2026, 4:56 p.m. |
| PD | Predicate disambiguation | batch_69ad9e078f7c8190813d9fcb4f5071fb |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:07 p.m.