Triple
T16120337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Li Auto |
E391117
|
entity |
| Predicate | hasBrandName |
P40804
|
FINISHED |
| Object | Li Auto |
E391117
|
NE 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: Li Auto | Statement: [Li Auto, hasBrandName, Li Auto]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Li Auto Context triple: [Li Auto, hasBrandName, Li Auto]
-
A.
Li Auto
chosen
Li Auto is a Chinese electric vehicle manufacturer known for its extended-range hybrid SUVs and focus on smart, family-oriented cars.
-
B.
XPeng
XPeng is a Chinese electric vehicle manufacturer known for its smart, tech-focused cars and advanced driver-assistance systems.
-
C.
BYD Auto
BYD Auto is a major Chinese automobile manufacturer best known for its electric and hybrid vehicles and rapid global expansion in the new energy vehicle market.
-
D.
Byton
Byton is a defunct Chinese electric vehicle startup that aimed to produce high-tech, connected smart cars for global markets.
-
E.
Zoomlion
Zoomlion is a major Chinese manufacturer of construction machinery and sanitation equipment, known as one of the largest heavy equipment companies in China.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e20200acac8190a47e6a917ff8dd34 |
completed | April 17, 2026, 9:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fff2a61e448190be1f8c79cae6c7ee |
completed | May 10, 2026, 2:51 a.m. |
Created at: April 10, 2026, 5 a.m.