Triple
T16120338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Li Auto |
E391117
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Li Xiang |
E1216355
|
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 Xiang | Statement: [Li Auto, alsoKnownAs, Li Xiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Li Xiang Context triple: [Li Auto, alsoKnownAs, Li Xiang]
-
A.
Li Xiang
chosen
Li Xiang is a Chinese entrepreneur best known as the founder and CEO of Li Auto, a leading electric vehicle manufacturer in China.
-
B.
Li Jiong
Li Jiong was a Tang dynasty imperial prince, known primarily as a son of Emperor Xianzong of Tang.
-
C.
Bao Xinhe
Bao Xinhe is a Chinese physical chemist and academic leader known for his research in catalysis and for serving as president of the University of Science and Technology of China.
-
D.
Li Wen
Li Wen, better known by his temple name Emperor Yizong of Tang, was a 9th-century emperor of the Tang dynasty in China.
-
E.
Huang Jianxin
Huang Jianxin is a prominent Chinese filmmaker known for his socially conscious, often satirical films that explore contemporary Chinese life and politics.
- 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_6a00606a3d5c8190a145ca35ce458f7e |
completed | May 10, 2026, 10:39 a.m. |
Created at: April 10, 2026, 5 a.m.