Triple
T1252308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baojun 730 |
E26903
|
entity |
| Predicate | facelift |
P25833
|
FINISHED |
| Object | 2017 facelift version |
—
|
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: 2017 facelift version | Statement: [Baojun 730, facelift, 2017 facelift version]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: facelift Context triple: [Baojun 730, facelift, 2017 facelift version]
-
A.
noseType
Indicates the specific shape or classification of a nose that an entity possesses.
-
B.
front
Indicates that one entity is located directly before or facing another entity along a primary viewing or movement direction.
-
C.
frontType
Indicates the type or category of a front (e.g., boundary or leading side) that one entity presents or forms relative to another.
-
D.
fine
Indicates that an authority imposes a monetary penalty on an entity for violating a rule, law, or agreement.
-
E.
skinCharacteristic
Indicates a relationship where an entity is associated with a particular quality, feature, or condition of its skin.
- 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_69a49487a9c48190ba9b05348fd1b53f |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf875cf48190b6781d41097ee39b |
completed | March 1, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6c977c8190a2bf3e8b67a59beb |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bc49693c8190978ec63a5171d342 |
completed | March 1, 2026, 10:23 p.m. |
Created at: March 1, 2026, 7:47 p.m.