Triple

T4321016
Position Surface form Disambiguated ID Type / Status
Subject Nantou E96515 entity
Predicate formerName P65 FINISHED
Object Xin’an
Xin’an is the former name of Nantou, a historic town in Shenzhen, China, that once served as an important administrative and commercial center in the region.
 E432671 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: Xin’an | Statement: [Nantou, formerName, Xin’an]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Xin’an
Context triple: [Nantou, formerName, Xin’an]
  • A. Lüshun
    Lüshun is a strategically important port city in northeastern China, historically known as Port Arthur and noted for its role in several major conflicts.
  • B. Heqing
    Heqing was an era name used during the Northern Qi dynasty in imperial China to designate a specific reign period.
  • C. Nankou
    Nankou is a town in Beijing’s Changping District known as a key gateway area near the Juyongguan section of the Great Wall.
  • D. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • E. Jinyang
    Jinyang is the historical name of the city now known as Taiyuan, a major urban and industrial center in northern China’s Shanxi 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: Xin’an
Triple: [Nantou, formerName, Xin’an]
Generated description
Xin’an is the former name of Nantou, a historic town in Shenzhen, China, that once served as an important administrative and commercial center in the region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Xin’an
Target entity description: Xin’an is the former name of Nantou, a historic town in Shenzhen, China, that once served as an important administrative and commercial center in the region.
  • A. Lüshun
    Lüshun is a strategically important port city in northeastern China, historically known as Port Arthur and noted for its role in several major conflicts.
  • B. Heqing
    Heqing was an era name used during the Northern Qi dynasty in imperial China to designate a specific reign period.
  • C. Nankou
    Nankou is a town in Beijing’s Changping District known as a key gateway area near the Juyongguan section of the Great Wall.
  • D. Zhizhong
    Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
  • E. Jinyang
    Jinyang is the historical name of the city now known as Taiyuan, a major urban and industrial center in northern China’s Shanxi 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_69b345422aac81909ddbadae437d122e completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b35114ed2c8190949c5d8032d7b921 completed March 12, 2026, 11:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5db950f5c8190ba67d8e2f8da50dc completed March 14, 2026, 10:05 p.m.
NEDg Description generation batch_69b5dc578b08819095cbf6ba8470d3e0 completed March 14, 2026, 10:08 p.m.
NED2 Entity disambiguation (via description) batch_69b5dd1b03508190a47bb6fb93f22ad8 completed March 14, 2026, 10:11 p.m.
Created at: March 12, 2026, 11:12 p.m.