Triple
T1102171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shibuya Crossing |
E25403
|
entity |
| Predicate | hasPedestrianSignalPattern |
P23121
|
FINISHED |
| Object | scramble crossing |
—
|
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: scramble crossing | Statement: [Shibuya Crossing, hasPedestrianSignalPattern, scramble crossing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPedestrianSignalPattern Context triple: [Shibuya Crossing, hasPedestrianSignalPattern, scramble crossing]
-
A.
hasPedestrianTrafficLevel
Indicates the level or intensity of pedestrian traffic associated with a given location or pathway.
-
B.
hasPedestrianArea
Indicates that a location or zone includes a designated area intended for pedestrian use only or primarily.
-
C.
hasPedestrianPlazaOn
Indicates that a pedestrian plaza is located on, or directly associated with, a specified surface, structure, or area.
-
D.
hasTrafficControl
Indicates that some form of traffic management or regulation mechanism is present or applied to a given route, intersection, or transportation element.
-
E.
pedestrianOnly
Indicates that a path, area, or route is designated exclusively for pedestrians and prohibits vehicle access.
- 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.