Triple

T1745977
Position Surface form Disambiguated ID Type / Status
Subject Eric Liddell E38335 entity
Predicate workLocation P7 FINISHED
Object Xiaochang
Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
 E215412 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: Xiaochang | Statement: [Eric Liddell, workLocation, Xiaochang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Xiaochang
Context triple: [Eric Liddell, workLocation, Xiaochang]
  • A. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • B. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • C. Xinyu
    Xinyu is a prefecture-level industrial city located in central Jiangxi Province in southeastern China.
  • D. Changling
    Changling is the largest and best-preserved mausoleum within Beijing’s Ming Tombs complex, built for the Yongle Emperor and his empress.
  • E. Chongxin
    Chongxin is the Chinese given name of Joe Tsai, the Taiwanese-Canadian co-founder and executive vice chairman of Alibaba Group.
  • 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: Xiaochang
Triple: [Eric Liddell, workLocation, Xiaochang]
Generated description
Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Xiaochang
Target entity description: Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
  • A. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • B. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • C. Xinyu
    Xinyu is a prefecture-level industrial city located in central Jiangxi Province in southeastern China.
  • D. Changling
    Changling is the largest and best-preserved mausoleum within Beijing’s Ming Tombs complex, built for the Yongle Emperor and his empress.
  • E. Chongxin
    Chongxin is the Chinese given name of Joe Tsai, the Taiwanese-Canadian co-founder and executive vice chairman of Alibaba Group.
  • 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_69a8862b01a48190ab47209063af82d9 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa63e93f3481909f61d581f7a5bdfa completed March 6, 2026, 5:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69adf3bc8fe8819085a8adaf9dcd5c1b completed March 8, 2026, 10:10 p.m.
NEDg Description generation batch_69adf494f0288190bf77285d18fcd3d9 completed March 8, 2026, 10:13 p.m.
NED2 Entity disambiguation (via description) batch_69adf51f49488190b9b0465b4da685c1 completed March 8, 2026, 10:15 p.m.
Created at: March 4, 2026, 7:31 p.m.