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.