Triple
T10293540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tobey Marshall |
E241424
|
entity |
| Predicate | hasDrivingStyle |
P92948
|
FINISHED |
| Object | aggressive but precise |
—
|
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: aggressive but precise | Statement: [Tobey Marshall, hasDrivingStyle, aggressive but precise]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDrivingStyle Context triple: [Tobey Marshall, hasDrivingStyle, aggressive but precise]
-
A.
hasDrivingSkill
Indicates that an entity possesses the ability or competence to operate and control a vehicle.
-
B.
hasDrivingConditions
Indicates that a particular route, area, or time period is associated with specific driving conditions (such as weather, traffic, or road surface state) that affect how vehicles can be driven.
-
C.
hasDriverCategory
Indicates that an entity (such as a vehicle or driving role) is associated with a specific driver category or license class.
-
D.
drivingExperience
Indicates the extent or history of a person's involvement in driving vehicles, typically measured by duration, frequency, or level of skill.
-
E.
driveType
Indicates the type or configuration of the drive mechanism used to power or propel an entity.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2d46fb08190b7694290692e47dc |
completed | April 7, 2026, 9:48 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f35e548190be3b4d92d65d2d20 |
completed | April 7, 2026, 9:44 a.m. |
| PDg | Predicate description generation | batch_69d4d29d7cf08190acd70cee634c5cdb |
completed | April 7, 2026, 9:47 a.m. |
Created at: April 6, 2026, 11:42 a.m.