Triple
T1102193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shibuya Crossing |
E25403
|
entity |
| Predicate | roadTraffic |
P23123
|
FINISHED |
| Object | multiDirectionMotorVehicleTraffic |
—
|
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: multiDirectionMotorVehicleTraffic | Statement: [Shibuya Crossing, roadTraffic, multiDirectionMotorVehicleTraffic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roadTraffic Context triple: [Shibuya Crossing, roadTraffic, multiDirectionMotorVehicleTraffic]
-
A.
trafficDirection
Indicates the direction in which traffic is intended or allowed to move relative to a given reference point or segment.
-
B.
trafficLevel
Indicates the degree of congestion or flow intensity present in a transportation network or route at a given time.
-
C.
roadSystem
Indicates a relationship where multiple roads are organized and connected as part of a larger, integrated transportation network or infrastructure.
-
D.
hasCommuterTraffic
Indicates that there is regular, recurring traffic flow associated with people traveling between their homes and places of work or study.
-
E.
openedToRoadTraffic
Indicates that something, such as a structure or route, has begun allowing regular use by vehicles or road users.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9c21c2c8190a34d91a7afed23a9 |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b7472c848190b0643872f67084a2 |
completed | March 1, 2026, 10:01 p.m. |
| PDg | Predicate description generation | batch_69a4b7da38888190a118ef20ce4ae9aa |
completed | March 1, 2026, 10:04 p.m. |
Created at: March 1, 2026, 7:43 p.m.