Triple

T8618333
Position Surface form Disambiguated ID Type / Status
Subject Zhou state E204097 entity
Predicate movedCapitalTo P19570 FINISHED
Object Feng
Feng was an early capital city of the Zhou dynasty in ancient China, serving as a key political and cultural center before later relocations.
 E746748 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: Feng | Statement: [Zhou state, movedCapitalTo, Feng]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Feng
Context triple: [Zhou state, movedCapitalTo, Feng]
  • A. Feng
    Feng is a Chinese surname borne by various notable figures in Chinese history and culture.
  • B. Shenfeng
    Shenfeng was a historical Chinese era name used during the reign of Sun Quan, ruler of Eastern Wu in the Three Kingdoms period.
  • C. Sulamutag Feng
    Sulamutag Feng is the highest peak in China’s remote Altyn-Tagh mountain range on the northern edge of the Tibetan Plateau.
  • D. Feng Qingfeng
    Feng Qingfeng is a Chinese automotive executive known for leading and overseeing strategic direction at the British sports car manufacturer Lotus Cars.
  • E. Nie Fengzhi
    Nie Fengzhi was a Chinese military officer and general who rose to prominence in the 20th century after receiving formal training at the Yunnan Military Academy.
  • 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: Feng
Triple: [Zhou state, movedCapitalTo, Feng]
Generated description
Feng was an early capital city of the Zhou dynasty in ancient China, serving as a key political and cultural center before later relocations.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Feng
Target entity description: Feng was an early capital city of the Zhou dynasty in ancient China, serving as a key political and cultural center before later relocations.
  • A. Feng
    Feng is a Chinese surname borne by various notable figures in Chinese history and culture.
  • B. Shenfeng
    Shenfeng was a historical Chinese era name used during the reign of Sun Quan, ruler of Eastern Wu in the Three Kingdoms period.
  • C. Sulamutag Feng
    Sulamutag Feng is the highest peak in China’s remote Altyn-Tagh mountain range on the northern edge of the Tibetan Plateau.
  • D. Feng Qingfeng
    Feng Qingfeng is a Chinese automotive executive known for leading and overseeing strategic direction at the British sports car manufacturer Lotus Cars.
  • E. Nie Fengzhi
    Nie Fengzhi was a Chinese military officer and general who rose to prominence in the 20th century after receiving formal training at the Yunnan Military Academy.
  • 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_69ca832ceab8819096e4a9f546695079 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc4712e74c81908b607a0ab2f9a361 completed March 31, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69cebbcb910881909088da53c6e31ae3 completed April 2, 2026, 6:56 p.m.
NEDg Description generation batch_69cebcc22d208190801b4ec58614dfcb completed April 2, 2026, 7 p.m.
NED2 Entity disambiguation (via description) batch_69cebdf3f288819088d83165c741d092 completed April 2, 2026, 7:05 p.m.
Created at: March 30, 2026, 6:26 p.m.