Triple
T8532532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nanning–Guangzhou High-Speed Railway |
E201989
|
entity |
| Predicate | significantlyReducesTravelTimeBetween |
P12934
|
FINISHED |
| Object | Nanning and Guangzhou |
—
|
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: Nanning and Guangzhou | Statement: [Nanning–Guangzhou High-Speed Railway, significantlyReducesTravelTimeBetween, Nanning and Guangzhou]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: significantlyReducesTravelTimeBetween Context triple: [Nanning–Guangzhou High-Speed Railway, significantlyReducesTravelTimeBetween, Nanning and Guangzhou]
-
A.
significantlyShortensRouteBetween
chosen
Indicates that one entity provides a connection between two others that makes the path or travel distance between them substantially shorter than alternative routes.
-
B.
travelTimeCategory
Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
-
C.
commutesBetween
Indicates a regular pattern of travel back and forth between two locations, typically for work, study, or routine activities.
-
D.
facilitatesTrafficFlowBetween
Indicates that one entity enables, supports, or improves the movement of traffic between two other entities or locations.
-
E.
relievesTrafficFrom
Indicates that one entity reduces or alleviates traffic congestion that would otherwise occur on 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_69ca832355b08190b8b6a4ab4a4a3554 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe676e2ac8190b65a3d2a935776fd |
completed | March 31, 2026, 3:21 p.m. |
| PD | Predicate disambiguation | batch_69cbd111bf988190be98c92a607c6456 |
completed | March 31, 2026, 1:50 p.m. |
Created at: March 30, 2026, 6:17 p.m.