Triple
T15351010
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkswagen plant Salzgitter |
E367049
|
entity |
| Predicate | usesManufacturingType |
P118224
|
FINISHED |
| Object | mass production |
—
|
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: mass production | Statement: [Volkswagen plant Salzgitter, usesManufacturingType, mass production]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesManufacturingType Context triple: [Volkswagen plant Salzgitter, usesManufacturingType, mass production]
-
A.
usesProductionResourcesOf
Indicates that one entity makes use of the production resources (such as facilities, equipment, or infrastructure) that belong to or are operated by another entity.
-
B.
componentTypeProduced
Indicates that one entity produces or manufactures a specific type of component as an output of its activity or process.
-
C.
usesProductionModel
Indicates that one entity employs or relies on another entity as its primary or official production model in practice.
-
D.
hasIndustrialCompany
Indicates that one entity possesses, controls, or is associated with an industrial company.
-
E.
equipmentTypeProduced
Indicates that an entity produces or manufactures a particular type or category of equipment.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e290efc8190b22c95dcd3e5f57f |
completed | April 16, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69deca991e5081908b0df3d1ee7d5338 |
completed | April 14, 2026, 11:15 p.m. |
| PDg | Predicate description generation | batch_69decf2e413481909d9180a8d78d2c17 |
completed | April 14, 2026, 11:35 p.m. |
Created at: April 10, 2026, 3:17 a.m.