Triple
T6421696
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dongfeng Liuzhou Motor |
E127957
|
entity |
| Predicate | vehicleCategoryProduced |
P1776
|
FINISHED |
| Object | light commercial vehicles |
—
|
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: light commercial vehicles | Statement: [Dongfeng Liuzhou Motor, vehicleCategoryProduced, light commercial vehicles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleCategoryProduced Context triple: [Dongfeng Liuzhou Motor, vehicleCategoryProduced, light commercial vehicles]
-
A.
vehicleDeveloped
Indicates that an entity (such as a person, organization, or group) created, designed, or otherwise developed a particular vehicle.
-
B.
vehicleType
chosen
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
C.
vehicleFamily
Indicates that two vehicles belong to the same family or category based on shared design, platform, or lineage.
-
D.
carModel
Indicates the specific model designation of a car within a particular make or brand.
-
E.
carManufacturer
Indicates that one entity is the company that produces or manufactures the car represented by the other entity.
- 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_69c0083815208190a9b299b8e0640218 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c06903f67c8190a1e5babeede4e183 |
completed | March 22, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69c060f5d4e481909d1366190607b586 |
completed | March 22, 2026, 9:36 p.m. |
Created at: March 22, 2026, 4:43 p.m.