Triple
T2467649
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wuhan–Guangzhou High-Speed Railway |
E55289
|
entity |
| Predicate | bridgeAndTunnelRatio |
P40026
|
FINISHED |
| Object | high proportion of bridges and tunnels |
—
|
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: high proportion of bridges and tunnels | Statement: [Wuhan–Guangzhou High-Speed Railway, bridgeAndTunnelRatio, high proportion of bridges and tunnels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bridgeAndTunnelRatio Context triple: [Wuhan–Guangzhou High-Speed Railway, bridgeAndTunnelRatio, high proportion of bridges and tunnels]
-
A.
hasBridgeTunnel
Indicates that there exists a bridge or tunnel connection between two locations or structures.
-
B.
bridgeType
Indicates the specific kind or classification of a bridge associated with an entity.
-
C.
hasNumberOfBridges
Indicates the quantitative relationship specifying how many bridges are associated with a given entity.
-
D.
bridgeLocation
Indicates the specific place or area where a bridge is situated or spans.
-
E.
bridgeUsed
Indicates that a bridge is utilized or traversed by an entity to cross between two locations or points.
- F. None of above. chosen
Provenance (4 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_69ab49e3622c8190ad22afa2c4fbb807 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd2bc7b5481908b3664495e99f1a4 |
completed | March 7, 2026, 7:24 a.m. |
| PD | Predicate disambiguation | batch_69abd0b3ea308190a6d8499c2a542c50 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd2baee308190bdaa41ef1f6bc9cc |
completed | March 7, 2026, 7:24 a.m. |
Created at: March 6, 2026, 9:44 p.m.