Triple
T30712994
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkswagen Tiguan (second generation) |
E781941
|
entity |
| Predicate | LWBNameInChina |
P100621
|
FINISHED |
| Object | Volkswagen Tiguan L |
—
|
NE NERFINISHED |
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: Volkswagen Tiguan L | Statement: [Volkswagen Tiguan (second generation), LWBNameInChina, Volkswagen Tiguan L]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: LWBNameInChina Context triple: [Volkswagen Tiguan (second generation), LWBNameInChina, Volkswagen Tiguan L]
-
A.
nameInChinese
Indicates that an entity has a specific written name or label expressed in the Chinese language.
-
B.
ChineseDesignation
chosen
Indicates that one entity serves as the Chinese-language name, title, or designation for another entity.
-
C.
taiwaneseName
Indicates that one entity is the Taiwanese name or Taiwanese-language designation of another entity.
-
D.
bearerNameInPinyin
Indicates that the bearer’s name is represented in its pinyin (romanized Chinese) form.
-
E.
countryNameInChinese
Indicates the Chinese-language name used to refer to a given country.
- 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_69f224acd24481908ed5f96f0d69b5dd |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f68f670b608190a0b6ab60d722b4e0 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f68b7b03488190b1db5fde4c7dd6e5 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 29, 2026, 8:35 p.m.