Triple
T2956702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Citroën Ami |
E79947
|
entity |
| Predicate | chassisCategory |
P1776
|
FINISHED |
| Object | L6e light quadricycle (in many EU markets) |
—
|
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: L6e light quadricycle (in many EU markets) | Statement: [Citroën Ami, chassisCategory, L6e light quadricycle (in many EU markets)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chassisCategory Context triple: [Citroën Ami, chassisCategory, L6e light quadricycle (in many EU markets)]
-
A.
chassis
Indicates that one entity serves as the structural frame or supporting base (chassis) for another entity.
-
B.
chassisMaterialFeature
Indicates that an entity has a chassis characterized by a specific material-related feature or property.
-
C.
chassisSupplier
Indicates that one entity serves as the supplier or provider of the chassis for another entity.
-
D.
carbodyMaterial
Indicates the material from which a vehicle’s body or main structural shell is made.
-
E.
vehicleType
chosen
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_69ad8b1276588190a374a0b12e0f7bdf |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad992998f08190ac9428310983172e |
completed | March 8, 2026, 3:43 p.m. |
| PD | Predicate disambiguation | batch_69ad960c5c8881909d679912bd7d78f3 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:57 p.m.