Triple

T1624811
Position Surface form Disambiguated ID Type / Status
Subject Henan Province E35116 entity
Predicate hasMajorCity P316 FINISHED
Object Xinxiang
Xinxiang is a prefecture-level industrial and transportation hub city located in northern Henan Province, China.
 E214214 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: Xinxiang | Statement: [Henan Province, hasMajorCity, Xinxiang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Xinxiang
Context triple: [Henan Province, hasMajorCity, Xinxiang]
  • A. Liuyang
    Liuyang is a county-level city in Hunan Province, China, known for its fireworks industry and cultural heritage.
  • B. 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.
  • C. Bozhou
    Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
  • D. 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.
  • E. Hengshui
    Hengshui is a prefecture-level city in southeastern Hebei Province, China, known for its traditional culture, agriculture, and growing industrial base.
  • 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: Xinxiang
Triple: [Henan Province, hasMajorCity, Xinxiang]
Generated description
Xinxiang is a prefecture-level industrial and transportation hub city located in northern Henan Province, China.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Xinxiang
Target entity description: Xinxiang is a prefecture-level industrial and transportation hub city located in northern Henan Province, China.
  • A. Liuyang
    Liuyang is a county-level city in Hunan Province, China, known for its fireworks industry and cultural heritage.
  • B. 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.
  • C. Bozhou
    Bozhou is a historic city in northern Anhui Province, China, known as a major center of traditional Chinese medicine and ancient culture.
  • D. 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.
  • E. Hengshui
    Hengshui is a prefecture-level city in southeastern Hebei Province, China, known for its traditional culture, agriculture, and growing industrial base.
  • 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_69a886023194819080a3fccd6e325d0e completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909d19b008190b2224717b2909a78 completed March 5, 2026, 4:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69adeac40c608190800da8b029ef065a completed March 8, 2026, 9:31 p.m.
NEDg Description generation batch_69adeb9dccf48190800ddd282331c4b4 completed March 8, 2026, 9:35 p.m.
NED2 Entity disambiguation (via description) batch_69adf023e39c8190a2651b6c1e59a2ee completed March 8, 2026, 9:54 p.m.
Created at: March 4, 2026, 7:28 p.m.