Triple

T11081257
Position Surface form Disambiguated ID Type / Status
Subject Peng Tsu Ying E261997 entity
Predicate givenName P17 FINISHED
Object Tsu Ying
Tsu Ying is the given name of Peng Tsu Ying, likely identifying an individual of Chinese origin.
E261997 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: Tsu Ying | Statement: [Peng Tsu Ying, givenName, Tsu Ying]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tsu Ying
Context triple: [Peng Tsu Ying, givenName, Tsu Ying]
  • A. Yüeh-chih
    Yüeh-chih were an ancient Indo-European nomadic people of Central Asia who played a key role in the formation of the Kushan Empire and the cultural exchanges along the Silk Road.
  • B. Peng Tsu Ying
    Peng Tsu Ying is an individual notable for bearing the Chinese surname Peng, though specific widely known public details about their life or achievements are not well documented.
  • C. Chang Ya-juo
    Chang Ya-juo was a Taiwanese woman known primarily as the partner of Chiang Ching-kuo and the mother of his son Chiang Hsiao-wen.
  • D. Tan Chingfen
    Tan Chingfen is a philanthropist whose major charitable contributions led to a prominent naming recognition at the University of Massachusetts Chan Medical School.
  • E. Hsia-men
    Hsia-men is an older romanized form of the name for Xiamen, a major port city on the southeast coast of China in Fujian Province.
  • 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: Tsu Ying
Triple: [Peng Tsu Ying, givenName, Tsu Ying]
Generated description
Tsu Ying is the given name of Peng Tsu Ying, likely identifying an individual of Chinese origin.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tsu Ying
Target entity description: Tsu Ying is the given name of Peng Tsu Ying, likely identifying an individual of Chinese origin.
  • A. Yüeh-chih
    Yüeh-chih were an ancient Indo-European nomadic people of Central Asia who played a key role in the formation of the Kushan Empire and the cultural exchanges along the Silk Road.
  • B. Peng Tsu Ying chosen
    Peng Tsu Ying is an individual notable for bearing the Chinese surname Peng, though specific widely known public details about their life or achievements are not well documented.
  • C. Chang Ya-juo
    Chang Ya-juo was a Taiwanese woman known primarily as the partner of Chiang Ching-kuo and the mother of his son Chiang Hsiao-wen.
  • D. Tan Chingfen
    Tan Chingfen is a philanthropist whose major charitable contributions led to a prominent naming recognition at the University of Massachusetts Chan Medical School.
  • E. Hsia-men
    Hsia-men is an older romanized form of the name for Xiamen, a major port city on the southeast coast of China in Fujian Province.
  • F. None of above.

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_69d6aa9983c08190b0ef61603b69feac completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d79996d9408190b159d14b23c25ed1 completed April 9, 2026, 12:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69e441b1b4dc8190a572d4d6269540cd completed April 19, 2026, 2:45 a.m.
NEDg Description generation batch_69e44c0606408190819b9d3fd58f818f completed April 19, 2026, 3:29 a.m.
NED2 Entity disambiguation (via description) batch_69e4510dc55081908f89aab15726b2a8 completed April 19, 2026, 3:50 a.m.
Created at: April 8, 2026, 9:27 p.m.