Triple
T5457091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaiser Jeep |
E122504
|
entity |
| Predicate | brandSpecialization |
P18486
|
FINISHED |
| Object | four-wheel-drive vehicles |
—
|
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: four-wheel-drive vehicles | Statement: [Kaiser Jeep, brandSpecialization, four-wheel-drive vehicles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: brandSpecialization Context triple: [Kaiser Jeep, brandSpecialization, four-wheel-drive vehicles]
-
A.
marketSpecialization
Indicates a relationship where an entity focuses its activities, products, or services on serving a specific segment or niche of a broader market.
-
B.
brandFocus
chosen
Indicates that a brand primarily concentrates its efforts, messaging, or resources on a particular target, theme, or market segment.
-
C.
styleSpecialty
Indicates a relationship where an entity’s expertise, focus, or specialization is in a particular style or stylistic approach.
-
D.
brandSegment
Indicates the specific market segment or customer group that a brand is targeted toward or associated with.
-
E.
creatorSpecialization
Indicates the specific field, discipline, or area of expertise in which a creator primarily works or is specialized.
- 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_69bd46424248819085282ddf50a565f3 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd927c946c8190aef40679199fede3 |
completed | March 20, 2026, 6:31 p.m. |
| PD | Predicate disambiguation | batch_69bd91a0d96c8190bd1299edbf764bbb |
completed | March 20, 2026, 6:27 p.m. |
Created at: March 20, 2026, 2:08 p.m.