Triple
T22408740
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kynsey Road |
E553943
|
entity |
| Predicate | hasTypicalTrafficPattern |
P29452
|
FINISHED |
| Object | peak-hour congestion |
—
|
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: peak-hour congestion | Statement: [Kynsey Road, hasTypicalTrafficPattern, peak-hour congestion]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalTrafficPattern Context triple: [Kynsey Road, hasTypicalTrafficPattern, peak-hour congestion]
-
A.
hasTrafficPattern
chosen
Indicates that there is a characteristic or recurring flow of traffic associated with an entity, such as its typical volume, direction, or timing of movement.
-
B.
hasTrafficMode
Indicates the mode or type of traffic associated with or applicable to an entity (e.g., pedestrian, vehicular, public transit).
-
C.
hasCargoTrafficType
Indicates that an entity is associated with a specific type or category of cargo traffic it handles or supports.
-
D.
hasTrafficFeature
Indicates that an entity possesses or is associated with a specific traffic-related characteristic, element, or infrastructure feature.
-
E.
hasPassengerTrafficFrom
Indicates that an entity receives or handles passenger traffic originating from another entity.
- 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_69e11e4e6ce8819085a1e06d886bf21c |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f158badc008190a3f5afb520a25e5f |
completed | April 29, 2026, 1:02 a.m. |
| PD | Predicate disambiguation | batch_69e8989495bc81909d2699fce5992e28 |
completed | April 22, 2026, 9:44 a.m. |
Created at: April 16, 2026, 8:46 p.m.