Triple
T5833518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alfa Romeo 159 |
E129407
|
entity |
| Predicate | faceliftChange |
P35748
|
FINISHED |
| Object | weight reduction compared to early models |
—
|
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: weight reduction compared to early models | Statement: [Alfa Romeo 159, faceliftChange, weight reduction compared to early models]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: faceliftChange Context triple: [Alfa Romeo 159, faceliftChange, weight reduction compared to early models]
-
A.
faceliftOf
Indicates that one entity is a redesigned, updated, or cosmetically improved version of another existing entity.
-
B.
facelift
Indicates that an entity undergoes a cosmetic surgical procedure intended to tighten or rejuvenate its facial appearance.
-
C.
faceliftFeature
chosen
Indicates that one entity is a specific feature, component, or aspect involved in a facelift procedure or facelift-related modification of the other entity.
-
D.
faceliftYear
Indicates the year in which an entity underwent a significant redesign, update, or cosmetic revision.
-
E.
frontLineChange
Indicates a change in the position or configuration of the primary boundary or leading edge between opposing sides.
- 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_69c0084af79c81908af128ccc29983d0 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c044ab0a048190b84be40fb13c0f50 |
completed | March 22, 2026, 7:36 p.m. |
| PD | Predicate disambiguation | batch_69c03341e5888190a5f219b6f92cb161 |
completed | March 22, 2026, 6:21 p.m. |
Created at: March 22, 2026, 3:54 p.m.