Triple
T14567952
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yangquan |
E341834
|
entity |
| Predicate | hasVehicleRegistrationCode |
P1173
|
FINISHED |
| Object |
晋C
晋C is the vehicle registration code assigned to motor vehicles registered in Yangquan, a city in Shanxi Province, China.
|
E1105788
|
NE FINISHED |
How this triple was built (4 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: 晋C | Statement: [Yangquan, hasVehicleRegistrationCode, 晋C]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 晋C Context triple: [Yangquan, hasVehicleRegistrationCode, 晋C]
-
A.
Caterham
Caterham is a town in the Tandridge district of Surrey, England, known as a commuter settlement on the edge of the London metropolitan area.
-
B.
Chater
Chater was the original name of Hong Kong’s Central MTR station, a major interchange hub on the city’s rapid transit network.
-
C.
Chesterfield
Chesterfield is a historic market town in Derbyshire, England, best known for its distinctive crooked church spire and its role in the region’s industrial development.
-
D.
Chesterfield
Chesterfield is a historic American cigarette brand known for its long-standing presence in the tobacco market and extensive advertising in the 20th century.
-
E.
Chesterfield
Chesterfield is a small town in Cheshire County, New Hampshire, known for its rural character and scenic location along the Connecticut River.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: 晋C Triple: [Yangquan, hasVehicleRegistrationCode, 晋C]
Generated description
晋C is the vehicle registration code assigned to motor vehicles registered in Yangquan, a city in Shanxi Province, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 晋C Target entity description: 晋C is the vehicle registration code assigned to motor vehicles registered in Yangquan, a city in Shanxi Province, China.
-
A.
Caterham
Caterham is a town in the Tandridge district of Surrey, England, known as a commuter settlement on the edge of the London metropolitan area.
-
B.
Chater
Chater was the original name of Hong Kong’s Central MTR station, a major interchange hub on the city’s rapid transit network.
-
C.
Chesterfield
Chesterfield is a historic market town in Derbyshire, England, best known for its distinctive crooked church spire and its role in the region’s industrial development.
-
D.
Chesterfield
Chesterfield is a historic American cigarette brand known for its long-standing presence in the tobacco market and extensive advertising in the 20th century.
-
E.
Chesterfield
Chesterfield is a small town in Cheshire County, New Hampshire, known for its rural character and scenic location along the Connecticut River.
- F. None of above. chosen
Provenance (5 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_69d822dcc6248190bed689984bceb0e2 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb38d89fc819086709fd3607b835f |
completed | April 14, 2026, 9:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd8ac669cc819083e05620b1e8c370 |
completed | May 8, 2026, 7:03 a.m. |
| NEDg | Description generation | batch_69fd8c5b09448190ad084746a6dd23f5 |
completed | May 8, 2026, 7:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd8d61f1f88190848c8f6095737897 |
completed | May 8, 2026, 7:14 a.m. |
Created at: April 10, 2026, 1:23 a.m.