Triple
T16266160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Holden HK Belmont |
E394879
|
entity |
| Predicate | trimLevelPosition |
P11486
|
FINISHED |
| Object | base commercial model |
—
|
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: base commercial model | Statement: [Holden HK Belmont, trimLevelPosition, base commercial model]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: trimLevelPosition Context triple: [Holden HK Belmont, trimLevelPosition, base commercial model]
-
A.
trimLevel
chosen
Indicates the specific configuration or package level of features or options applied to an item, typically distinguishing variants within the same base model.
-
B.
trimLevelAbove
Indicates that one entity’s trim level is higher or more upgraded than another’s.
-
C.
trimPosition
Indicates the specific location or segment within something (such as text, media, or data) where trimming or cutting is applied.
-
D.
trimLevelRelativeToLaramie
Indicates how an entity’s trim level compares to the trim level used as a reference in Laramie (e.g., higher, lower, or equivalent).
-
E.
trimPositionIn88Line
Indicates the position or location where trimming occurs within an 88-character (or 88-unit) line.
- 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_69d87f221d8081909b0b2063e7528ba2 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e245c839788190b974d1d0d2525b88 |
completed | April 17, 2026, 2:38 p.m. |
| PD | Predicate disambiguation | batch_69e219f259e88190bf49d8408c04178e |
completed | April 17, 2026, 11:30 a.m. |
Created at: April 10, 2026, 5:05 a.m.