Triple

T10110049
Position Surface form Disambiguated ID Type / Status
Subject Old Xiang E218215 entity
Predicate spokenIn P2266 FINISHED
Object Wugang
Wugang is a county-level city in Hunan Province, China, known for its role in preserving the Old Xiang variety of the Xiang Chinese language.
 E842125 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: Wugang | Statement: [Old Xiang, spokenIn, Wugang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wugang
Context triple: [Old Xiang, spokenIn, Wugang]
  • A. Fengfeng Mining District
    Fengfeng Mining District is an administrative district of Handan in Hebei Province, China, historically centered around coal mining and related heavy industry.
  • B. Sihui
    Sihui is a major Beijing Subway station in eastern Beijing that serves as a key interchange and endpoint for multiple metro lines.
  • C. Suihua
    Suihua is a prefecture-level city in northeastern China known for its agricultural production and cold climate.
  • D. Wuping
    Wuping was an era name used during the Northern Qi dynasty in imperial China, marking a specific reign period within that dynasty’s rule.
  • E. Ma’anshan
    Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower Yangtze River.
  • 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: Wugang
Triple: [Old Xiang, spokenIn, Wugang]
Generated description
Wugang is a county-level city in Hunan Province, China, known for its role in preserving the Old Xiang variety of the Xiang Chinese language.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wugang
Target entity description: Wugang is a county-level city in Hunan Province, China, known for its role in preserving the Old Xiang variety of the Xiang Chinese language.
  • A. Fengfeng Mining District
    Fengfeng Mining District is an administrative district of Handan in Hebei Province, China, historically centered around coal mining and related heavy industry.
  • B. Sihui
    Sihui is a major Beijing Subway station in eastern Beijing that serves as a key interchange and endpoint for multiple metro lines.
  • C. Suihua
    Suihua is a prefecture-level city in northeastern China known for its agricultural production and cold climate.
  • D. Wuping
    Wuping was an era name used during the Northern Qi dynasty in imperial China, marking a specific reign period within that dynasty’s rule.
  • E. Ma’anshan
    Ma’anshan is an industrial city in eastern China known for its steel production and location along the lower Yangtze River.
  • 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_69ca83da93fc8190b54e44bc2b34857c completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cdd0cdb3c88190a74f75bf865664f3 completed April 2, 2026, 2:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2cc1805d08190bc39aadf1e84a569 completed April 5, 2026, 8:54 p.m.
NEDg Description generation batch_69d2cd8f0a688190a437b7e2d158c70c completed April 5, 2026, 9:01 p.m.
NED2 Entity disambiguation (via description) batch_69d2ce422e4c8190b54b94cdfa0c4c98 completed April 5, 2026, 9:04 p.m.
Created at: March 30, 2026, 9:03 p.m.