Triple

T15610784
Position Surface form Disambiguated ID Type / Status
Subject Toghon Temür E375283 entity
Predicate deathPlace P21 FINISHED
Object Yingchang
Yingchang was a city in northern China that served as a retreat and final refuge for the last Yuan emperor, Toghon Temür, after the dynasty’s collapse.
 E1167021 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: Yingchang | Statement: [Toghon Temür, deathPlace, Yingchang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yingchang
Context triple: [Toghon Temür, deathPlace, Yingchang]
  • A. Bingchang
    Bingchang is a Chinese given name, notably borne by diplomat and politician Fu Bingchang.
  • B. Xingyuan
    Xingyuan was the Chinese era name used during part of Emperor Dezong of Tang’s reign in the late eighth century.
  • C. Longqing
    Longqing was the era name of a brief but notable period of the Ming dynasty in China, associated with the reign of the Longqing Emperor in the 16th century.
  • D. Lechang
    Lechang is a county-level city administered by Shaoguan in northern Guangdong Province, China, known for its mountainous terrain and role as a regional transport and commercial hub.
  • E. Xiaochang
    Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
  • 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: Yingchang
Triple: [Toghon Temür, deathPlace, Yingchang]
Generated description
Yingchang was a city in northern China that served as a retreat and final refuge for the last Yuan emperor, Toghon Temür, after the dynasty’s collapse.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yingchang
Target entity description: Yingchang was a city in northern China that served as a retreat and final refuge for the last Yuan emperor, Toghon Temür, after the dynasty’s collapse.
  • A. Bingchang
    Bingchang is a Chinese given name, notably borne by diplomat and politician Fu Bingchang.
  • B. Xingyuan
    Xingyuan was the Chinese era name used during part of Emperor Dezong of Tang’s reign in the late eighth century.
  • C. Longqing
    Longqing was the era name of a brief but notable period of the Ming dynasty in China, associated with the reign of the Longqing Emperor in the 16th century.
  • D. Lechang
    Lechang is a county-level city administered by Shaoguan in northern Guangdong Province, China, known for its mountainous terrain and role as a regional transport and commercial hub.
  • E. Xiaochang
    Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
  • 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_69d85ccf2794819096cda4cbcb02d478 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04e8024948190a6c711f2e5c2aac4 completed April 16, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff56d76c108190aa3cae2d7e17c301 completed May 9, 2026, 3:46 p.m.
NEDg Description generation batch_69ff57c304188190afa695ae88cf0234 completed May 9, 2026, 3:50 p.m.
NED2 Entity disambiguation (via description) batch_69ff5920436c81909addad5bb4566ae9 completed May 9, 2026, 3:56 p.m.
Created at: April 10, 2026, 4:13 a.m.