Triple
T17574796
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magdalen Street, Oxford |
E428035
|
entity |
| Predicate | hasMotorTraffic |
P107210
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Magdalen Street, Oxford, hasMotorTraffic, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMotorTraffic Context triple: [Magdalen Street, Oxford, hasMotorTraffic, yes]
-
A.
hasTrafficFeature
Indicates that an entity possesses or is associated with a specific traffic-related characteristic, element, or infrastructure feature.
-
B.
hasTrafficSignals
Indicates that traffic control signals are present at or associated with a given location or roadway feature.
-
C.
hasTruckTraffic
Indicates that there is truck-related vehicular movement or flow occurring on or through a specified location or route.
-
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.
hasTrafficMode
chosen
Indicates the mode or type of traffic associated with or applicable to an entity (e.g., pedestrian, vehicular, public transit).
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4593403648190836266dbdb6cfc9f |
completed | April 19, 2026, 4:25 a.m. |
| PD | Predicate disambiguation | batch_69e3b4fd7d048190b54ee4c6155612a5 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:50 a.m.