Triple
T8440476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guizhou–Guangzhou High-Speed Railway |
E199337
|
entity |
| Predicate | travelTimeEffect |
P3830
|
FINISHED |
| Object | significantly shortened journey between Guiyang 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: significantly shortened journey between Guiyang and Guangzhou | Statement: [Guizhou–Guangzhou High-Speed Railway, travelTimeEffect, significantly shortened journey between Guiyang and Guangzhou]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelTimeEffect Context triple: [Guizhou–Guangzhou High-Speed Railway, travelTimeEffect, significantly shortened journey between Guiyang and Guangzhou]
-
A.
travelTimeCategory
Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
-
B.
transportationImpact
chosen
Indicates how one entity’s transportation-related activities or characteristics affect another entity or the surrounding environment.
-
C.
travelTimeTypical
Indicates the usual or expected amount of time it takes to travel between two locations under normal conditions.
-
D.
previousTravelTimeOnRoute
Indicates the duration of travel that occurred earlier on the same route before the current segment or time period.
-
E.
temporalEffect
Indicates a relationship where one event, state, or action produces consequences or changes that occur at a later time.
- 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_69ca8314cd6c8190a6b8c2a1096e18f3 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe30fba4081908bfdef3faf5baceb |
completed | March 31, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69cbd0f5a3648190beb53a139a2d5482 |
completed | March 31, 2026, 1:49 p.m. |
Created at: March 30, 2026, 6:08 p.m.