Triple
T4045873
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buick Regal |
E84063
|
entity |
| Predicate | notableMarketPositioning |
P53784
|
FINISHED |
| Object | near-luxury vehicle |
—
|
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: near-luxury vehicle | Statement: [Buick Regal, notableMarketPositioning, near-luxury vehicle]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableMarketPositioning Context triple: [Buick Regal, notableMarketPositioning, near-luxury vehicle]
-
A.
notableCommercial
Indicates that an entity is particularly well-known or significant in a commercial context, such as advertising, marketing, or business promotion.
-
B.
notableRegionOfBusiness
Indicates that a specified geographic region is a significant or primary area in which an entity conducts its business activities.
-
C.
notableSector
Indicates that an entity is particularly prominent, influential, or significant within a specified sector or industry.
-
D.
notableAdvantage
Indicates that one entity possesses a significant benefit, edge, or favorable quality over another entity or in a given context.
-
E.
notableCEO
Indicates that the subject is a chief executive officer who is widely recognized or distinguished in a notable way.
- 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_69aed930bd5c819083e7dcc14fc44f69 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb6135b481909d2be890a2140ff9 |
completed | March 9, 2026, 4:54 p.m. |
| PD | Predicate disambiguation | batch_69aef900386481909d04555a9ec9b0e3 |
completed | March 9, 2026, 4:44 p.m. |
| PDg | Predicate description generation | batch_69aef98ed84c81909dc86097df4afd4f |
completed | March 9, 2026, 4:47 p.m. |
Created at: March 9, 2026, 3:37 p.m.