Triple
T14737352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beijing–Tianjin Intercity Railway |
E346247
|
entity |
| Predicate | travelTimeBeijingTianjin |
P46906
|
FINISHED |
| Object | about 30 minutes |
—
|
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: about 30 minutes | Statement: [Beijing–Tianjin Intercity Railway, travelTimeBeijingTianjin, about 30 minutes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: travelTimeBeijingTianjin Context triple: [Beijing–Tianjin Intercity Railway, travelTimeBeijingTianjin, about 30 minutes]
-
A.
travelTimeBeijingToZhangjiakou
Indicates the amount of time required to travel from Beijing to Zhangjiakou.
-
B.
distanceFromBeijing_km
Indicates the physical distance, measured in kilometers, between a given place or object and Beijing.
-
C.
travelTimeCategory
Indicates the qualitative classification of how long a given travel or trip duration is (e.g., short, medium, long).
-
D.
approximateTravelTimeCoastToCoast
Indicates the estimated duration required to travel from one coast to the opposite coast.
-
E.
travelTimeTypical
chosen
Indicates the usual or expected amount of time it takes to travel between two locations under normal conditions.
- 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_69d822e6f1c88190bc494d491a907114 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec73264848190be23c5f0260cbe13 |
completed | April 14, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69de8bf9331481909582045cd567d91f |
completed | April 14, 2026, 6:48 p.m. |
Created at: April 10, 2026, 1:29 a.m.