Triple

T1575963
Position Surface form Disambiguated ID Type / Status
Subject Taro E33651 entity
Predicate kanjiVariant P17917 FINISHED
Object 多朗
多朗 is a Japanese masculine given name, written with kanji that can convey meanings such as “many” or “abundant” combined with “son” or “boy.”
 E179785 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: 多朗 | Statement: [Taro, kanjiVariant, 多朗]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 多朗
Context triple: [Taro, kanjiVariant, 多朗]
  • A. Changling
    Changling is the largest and best-preserved mausoleum within Beijing’s Ming Tombs complex, built for the Yongle Emperor and his empress.
  • B. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • C. Mengjiang
    Mengjiang was a Japanese puppet state established in Inner Mongolia during the Second Sino-Japanese War and World War II.
  • D. Lingang
    Lingang is a rapidly developing industrial and high-tech district in Shanghai, China, known for hosting major manufacturing facilities such as Tesla’s Gigafactory Shanghai.
  • E. Chongxin
    Chongxin is the Chinese given name of Joe Tsai, the Taiwanese-Canadian co-founder and executive vice chairman of Alibaba Group.
  • 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: 多朗
Triple: [Taro, kanjiVariant, 多朗]
Generated description
多朗 is a Japanese masculine given name, written with kanji that can convey meanings such as “many” or “abundant” combined with “son” or “boy.”
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 多朗
Target entity description: 多朗 is a Japanese masculine given name, written with kanji that can convey meanings such as “many” or “abundant” combined with “son” or “boy.”
  • A. Changling
    Changling is the largest and best-preserved mausoleum within Beijing’s Ming Tombs complex, built for the Yongle Emperor and his empress.
  • B. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • C. Mengjiang
    Mengjiang was a Japanese puppet state established in Inner Mongolia during the Second Sino-Japanese War and World War II.
  • D. Lingang
    Lingang is a rapidly developing industrial and high-tech district in Shanghai, China, known for hosting major manufacturing facilities such as Tesla’s Gigafactory Shanghai.
  • E. Chongxin
    Chongxin is the Chinese given name of Joe Tsai, the Taiwanese-Canadian co-founder and executive vice chairman of Alibaba Group.
  • 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_69a885f27a4c8190a4622252cdf54c00 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa61ddc9908190a4afca1c24400817 completed March 6, 2026, 5:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad402b44688190b02e6d146f009854 completed March 8, 2026, 9:23 a.m.
NEDg Description generation batch_69ad410ba7f881909dcee6e6fd56490f completed March 8, 2026, 9:27 a.m.
NED2 Entity disambiguation (via description) batch_69ad41ff0b0c8190b5429ed6952a0ce6 completed March 8, 2026, 9:31 a.m.
Created at: March 4, 2026, 7:27 p.m.