Triple
T16802053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chaguanas Main Road commercial strip |
E408380
|
entity |
| Predicate | hasVehicularActivityLevel |
P124688
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Chaguanas Main Road commercial strip, hasVehicularActivityLevel, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVehicularActivityLevel Context triple: [Chaguanas Main Road commercial strip, hasVehicularActivityLevel, high]
-
A.
hasVehicularUse
Indicates that something is used for, intended for, or associated with operation by vehicles or vehicular traffic.
-
B.
hasPrimaryVehicularAccessTo
Indicates that one location or entity serves as the main route or means by which vehicles can reach or enter another location or entity.
-
C.
hasVehicle
Indicates that one entity possesses, owns, or is assigned a vehicle.
-
D.
hasVehicleAccess
Indicates that an entity is permitted to use or enter a particular vehicle.
-
E.
hasTruckTraffic
Indicates that there is truck-related vehicular movement or flow occurring on or through a specified location or route.
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2c8fe58819087d3b83635255c34 |
completed | April 18, 2026, 4:35 p.m. |
| PD | Predicate disambiguation | batch_69e319d0fdb8819088425bd82431640f |
completed | April 18, 2026, 5:42 a.m. |
| PDg | Predicate description generation | batch_69e326bac94481908c082117553320f8 |
completed | April 18, 2026, 6:37 a.m. |
Created at: April 10, 2026, 5:22 a.m.