Triple
T3109984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanxi Province |
E64927
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Shuozhou
Shuozhou is a prefecture-level city in northern China known for its coal resources and historical sites within Shanxi Province.
|
E332942
|
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: Shuozhou | Statement: [Shanxi Province, hasMajorCity, Shuozhou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shuozhou Context triple: [Shanxi Province, hasMajorCity, Shuozhou]
-
A.
Datong
Datong is a historic industrial city in northern China known for its coal production and nearby cultural landmarks such as the Yungang Grottoes.
-
B.
Weinan
Weinan is a prefecture-level city in eastern Shaanxi Province, China, known for its historical sites and location near the Wei River.
-
C.
Bozhou
Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
-
D.
Yulin
Yulin is a prefecture-level city in northern China known for its coal resources and location on the Loess Plateau near the border with Inner Mongolia.
-
E.
Hucheng
Hucheng is the given name of Yang Hucheng, a prominent Chinese general and political figure best known for his role in the Xi'an Incident of 1936.
- 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: Shuozhou Triple: [Shanxi Province, hasMajorCity, Shuozhou]
Generated description
Shuozhou is a prefecture-level city in northern China known for its coal resources and historical sites within Shanxi Province.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shuozhou Target entity description: Shuozhou is a prefecture-level city in northern China known for its coal resources and historical sites within Shanxi Province.
-
A.
Datong
Datong is a historic industrial city in northern China known for its coal production and nearby cultural landmarks such as the Yungang Grottoes.
-
B.
Weinan
Weinan is a prefecture-level city in eastern Shaanxi Province, China, known for its historical sites and location near the Wei River.
-
C.
Bozhou
Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
-
D.
Yulin
Yulin is a prefecture-level city in northern China known for its coal resources and location on the Loess Plateau near the border with Inner Mongolia.
-
E.
Hucheng
Hucheng is the given name of Yang Hucheng, a prominent Chinese general and political figure best known for his role in the Xi'an Incident of 1936.
- 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_69ad857eeaf48190b34ebfdaa7a264cf |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada2a0ab2481908db50738ec3ad0fb |
completed | March 8, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b235a8949881908496a1413ffa2658 |
completed | March 12, 2026, 3:40 a.m. |
| NEDg | Description generation | batch_69b236962ee48190b37836e5fe6dbc37 |
completed | March 12, 2026, 3:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b237397e14819093a7192d28c59ad1 |
completed | March 12, 2026, 3:47 a.m. |
Created at: March 8, 2026, 3:04 p.m.