Triple
T15327186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Osaka Metro 20 series |
E366440
|
entity |
| Predicate | hasCarbody |
P19785
|
FINISHED |
| Object | stainless-steel double-skin structure |
—
|
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: stainless-steel double-skin structure | Statement: [Osaka Metro 20 series, hasCarbody, stainless-steel double-skin structure]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCarbody Context triple: [Osaka Metro 20 series, hasCarbody, stainless-steel double-skin structure]
-
A.
carbodyMaterial
chosen
Indicates the material from which a vehicle’s body or main structural shell is made.
-
B.
hasBodyColor
Indicates that an entity possesses a particular body color as one of its attributes.
-
C.
carBodyStyle
Indicates the specific body configuration or design style that characterizes a car (e.g., sedan, hatchback, SUV).
-
D.
bodyTypeDepicted
Indicates that one entity visually represents or portrays the physical body type of another entity.
-
E.
hasChassisType
Indicates that an entity is associated with or equipped with a specific type of chassis.
- 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_69d85a121520819093dcce999fdefe1a |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03dffd6f88190a0f031ee90c6a7d2 |
completed | April 16, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69deca9659f48190b8661df223ce5078 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:16 a.m.