Triple
T16420082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DS 3 |
E398793
|
entity |
| Predicate | faceliftDetail |
P45252
|
FINISHED |
| Object | rebranded from Citroën DS3 to DS 3 |
—
|
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: rebranded from Citroën DS3 to DS 3 | Statement: [DS 3, faceliftDetail, rebranded from Citroën DS3 to DS 3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: faceliftDetail Context triple: [DS 3, faceliftDetail, rebranded from Citroën DS3 to DS 3]
-
A.
faceliftOf
chosen
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
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.
faceliftCode
Indicates that an entity has undergone or is associated with a specific facelift procedure identified by a particular code.
-
E.
designerOfFacelift
Indicates that one entity is the designer or creator responsible for the facelift (a redesign or cosmetic update) of another entity.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e328f5c1bc8190a679f35bd6c0bc97 |
completed | April 18, 2026, 6:47 a.m. |
| PD | Predicate disambiguation | batch_69e22701d2288190bf8676050758f172 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:09 a.m.