Triple
T31581614
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokyu 3000 series EMU |
E805839
|
entity |
| Predicate | carBodyType |
P19785
|
FINISHED |
| Object | lightweight stainless steel |
—
|
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: lightweight stainless steel | Statement: [Tokyu 3000 series EMU, carBodyType, lightweight stainless steel]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carBodyType Context triple: [Tokyu 3000 series EMU, carBodyType, lightweight stainless steel]
-
A.
carBodyStyle
Indicates the specific body configuration or design style that characterizes a car (e.g., sedan, hatchback, SUV).
-
B.
bodyTypes
Indicates a relationship where one or more body types are associated with or assigned to an entity.
-
C.
carBodyProfile
Indicates the overall shape, contours, and structural outline that define a car’s external body form.
-
D.
carbodyMaterial
chosen
Indicates the material from which a vehicle’s body or main structural shell is made.
-
E.
vehicleType
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
- 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_69f348d3a86c8190a3e5e539a4dd125f |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a80977a88190bd64c02801a72e0b |
completed | May 3, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69f6a75656e081908739ed9e2f600e42 |
completed | May 3, 2026, 1:39 a.m. |
Created at: April 30, 2026, 10:23 p.m.