Triple

T1498621
Position Surface form Disambiguated ID Type / Status
Subject Changsha E29743 entity
Predicate hasCountyLevelCity P27799 FINISHED
Object Liuyang
Liuyang is a county-level city in Hunan Province, China, known for its fireworks industry and cultural heritage.
 E190796 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: Liuyang | Statement: [Changsha, hasCountyLevelCity, Liuyang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Liuyang
Context triple: [Changsha, hasCountyLevelCity, Liuyang]
  • A. Bozhou
    Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
  • B. Xiangyang
    Xiangyang is a historic prefecture-level city in northern Hubei Province, China, known for its strategic location on the Han River and well-preserved ancient city walls.
  • C. Anyang
    Anyang is an ancient city in northern China renowned as one of the historical capitals of the Shang dynasty and a major archaeological site.
  • D. Zhengzhou
    Zhengzhou is a major city in central China that serves as the capital of Henan Province and an important national transportation and industrial hub.
  • E. Xianning
    Xianning is a prefecture-level city in southeastern Hubei Province, China, known for its hot springs, karst landscapes, and historical sites.
  • 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: Liuyang
Triple: [Changsha, hasCountyLevelCity, Liuyang]
Generated description
Liuyang is a county-level city in Hunan Province, China, known for its fireworks industry and cultural heritage.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Liuyang
Target entity description: Liuyang is a county-level city in Hunan Province, China, known for its fireworks industry and cultural heritage.
  • A. Bozhou
    Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
  • B. Xiangyang
    Xiangyang is a historic prefecture-level city in northern Hubei Province, China, known for its strategic location on the Han River and well-preserved ancient city walls.
  • C. Anyang
    Anyang is an ancient city in northern China renowned as one of the historical capitals of the Shang dynasty and a major archaeological site.
  • D. Zhengzhou
    Zhengzhou is a major city in central China that serves as the capital of Henan Province and an important national transportation and industrial hub.
  • E. Xianning
    Xianning is a prefecture-level city in southeastern Hubei Province, China, known for its hot springs, karst landscapes, and historical sites.
  • 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_69a498dba1d8819093b46a3a8d2485f1 completed March 1, 2026, 7:51 p.m.
NER Named-entity recognition batch_69a4c6ef5ce88190a6b520525a6d42a3 completed March 1, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad797907ac81908ede43626798827d completed March 8, 2026, 1:28 p.m.
NEDg Description generation batch_69ad7a1223fc8190b7d62217c17f7517 completed March 8, 2026, 1:30 p.m.
NED2 Entity disambiguation (via description) batch_69ad7b0787c88190a59a815fa808ac6b completed March 8, 2026, 1:35 p.m.
Created at: March 1, 2026, 8:12 p.m.