Triple

T10380229
Position Surface form Disambiguated ID Type / Status
Subject Ma Xiangbo E244619 entity
Predicate givenName P17 FINISHED
Object Xiangbo
Xiangbo is the given name of Ma Xiangbo, a prominent Chinese Jesuit priest, educator, and co-founder of several influential modern universities in China.
 E859434 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: Xiangbo | Statement: [Ma Xiangbo, givenName, Xiangbo]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Xiangbo
Context triple: [Ma Xiangbo, givenName, Xiangbo]
  • A. Yuxiang
    Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
  • B. Xiaochang
    Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
  • C. Huaxiang
    Huaxiang is a subdistrict-level area within Beijing’s Fengtai District, known primarily as a residential and urban community zone.
  • D. Xiang
    Xiang is a major group of Chinese dialects spoken primarily in Hunan province, known for preserving many archaic features of Middle Chinese.
  • E. Xiang
    Xiang is the standard abbreviation and common short name used to refer to China’s Hunan Province.
  • 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: Xiangbo
Triple: [Ma Xiangbo, givenName, Xiangbo]
Generated description
Xiangbo is the given name of Ma Xiangbo, a prominent Chinese Jesuit priest, educator, and co-founder of several influential modern universities in China.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Xiangbo
Target entity description: Xiangbo is the given name of Ma Xiangbo, a prominent Chinese Jesuit priest, educator, and co-founder of several influential modern universities in China.
  • A. Yuxiang
    Yuxiang is a Chinese given name notably borne by the early 20th-century warlord and military leader Feng Yuxiang.
  • B. Xiaochang
    Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
  • C. Huaxiang
    Huaxiang is a subdistrict-level area within Beijing’s Fengtai District, known primarily as a residential and urban community zone.
  • D. Xiang
    Xiang is a major group of Chinese dialects spoken primarily in Hunan province, known for preserving many archaic features of Middle Chinese.
  • E. Xiang
    Xiang is the standard abbreviation and common short name used to refer to China’s Hunan Province.
  • 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_69d381b3e328819094b23b8edcd29b5a completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e991056c8190a981f717c51f1f72 completed April 7, 2026, 11:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69d7958803e88190a7bbeda4f2c6f32c completed April 9, 2026, 12:03 p.m.
NEDg Description generation batch_69d79784baa481909e57adda27578cc2 completed April 9, 2026, 12:11 p.m.
NED2 Entity disambiguation (via description) batch_69d7989f8dfc8190b1fe4429f7bb0283 completed April 9, 2026, 12:16 p.m.
Created at: April 6, 2026, 12:03 p.m.