Triple
T3802019
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maxus |
E91709
|
entity |
| Predicate | focusesOnMarketSegment |
P43327
|
FINISHED |
| Object | light commercial 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: light commercial vehicles | Statement: [Maxus, focusesOnMarketSegment, light commercial vehicles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: focusesOnMarketSegment Context triple: [Maxus, focusesOnMarketSegment, light commercial vehicles]
-
A.
targetMarket
Indicates the group of consumers or organizations that a product, service, or campaign is specifically intended and designed to reach.
-
B.
focusesOn
Indicates that one entity directs its attention, effort, or primary activity toward another entity or specific subject.
-
C.
marketSpecialization
chosen
Indicates a relationship where an entity focuses its activities, products, or services on serving a specific segment or niche of a broader market.
-
D.
brandFocus
Indicates that a brand primarily concentrates its efforts, messaging, or resources on a particular target, theme, or market segment.
-
E.
businessModelFocus
Indicates that one entity’s business model is centered on, tailored to, or primarily oriented around another entity or specific focus area.
- 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_69aed96354f48190a768966d6bd19b04 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aee8db8a288190afd1e3b9dcf02e97 |
completed | March 9, 2026, 3:35 p.m. |
| PD | Predicate disambiguation | batch_69aee7461abc8190945716f4b93e1a18 |
completed | March 9, 2026, 3:29 p.m. |
Created at: March 9, 2026, 3:15 p.m.