Triple
T30713079
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kvasiny, Czech Republic |
E781943
|
entity |
| Predicate | hasVehicleAssemblyPlant |
P25392
|
FINISHED |
| Object | Škoda Auto Kvasiny plant |
—
|
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: Škoda Auto Kvasiny plant | Statement: [Kvasiny, Czech Republic, hasVehicleAssemblyPlant, Škoda Auto Kvasiny plant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasVehicleAssemblyPlant Context triple: [Kvasiny, Czech Republic, hasVehicleAssemblyPlant, Škoda Auto Kvasiny plant]
-
A.
hasProductionFacilitiesIn
Indicates that an entity operates or owns production facilities located within a specified geographic area or jurisdiction.
-
B.
hasIndustrialPlant
chosen
Indicates that an entity possesses, operates, or is associated with an industrial plant facility.
-
C.
hasIndustrialCompany
Indicates that one entity possesses, controls, or is associated with an industrial company.
-
D.
hasAssemblyLine
Indicates that one entity possesses or operates an assembly line used for producing or assembling items.
-
E.
hasMajorAutomakerHeadquarters
Indicates that a location serves as the primary corporate headquarters for a major automobile manufacturing company.
- 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_69f6b967d5308190bbb66d0a8dd52612 |
completed | May 3, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69f6b6293188819080d5041ca0adb969 |
completed | May 3, 2026, 2:42 a.m. |
Created at: April 29, 2026, 8:35 p.m.