Triple
T30712993
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkswagen Tiguan (second generation) |
E781941
|
entity |
| Predicate | LWBNameInEurope |
P124697
|
FINISHED |
| Object | Volkswagen Tiguan Allspace |
—
|
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 Allspace | Statement: [Volkswagen Tiguan (second generation), LWBNameInEurope, Volkswagen Tiguan Allspace]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: LWBNameInEurope Context triple: [Volkswagen Tiguan (second generation), LWBNameInEurope, Volkswagen Tiguan Allspace]
-
A.
legalNameInEU
Indicates that the specified name is the official legal name of an entity as recognized within the European Union jurisdiction.
-
B.
hasNameInBelgium
Indicates that an entity is known or referred to by a particular name specifically within the context of Belgium.
-
C.
hasNameInLuxembourgish
Indicates that an entity is known or referred to by a specific name in the Luxembourgish language.
-
D.
countrySpecificName
chosen
Indicates that an entity has a name or label that is specific to, or used within, a particular country.
-
E.
eraNameUsedInDocuments
Indicates that a particular era name is used as a temporal reference in the specified documents.
- 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_69f68c1fd1e081908fa0a55e82f3030b |
completed | May 2, 2026, 11:43 p.m. |
| PD | Predicate disambiguation | batch_69f6861170d08190bb98be609d436f84 |
completed | May 2, 2026, 11:17 p.m. |
Created at: April 29, 2026, 8:35 p.m.