Triple
T15516374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peng-Peng Lee |
E368843
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Peng-Peng
Peng-Peng is the nickname of Peng-Peng Lee, a Canadian-American artistic gymnast known for her elite career and standout NCAA performances with UCLA.
|
E1162021
|
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: Peng-Peng | Statement: [Peng-Peng Lee, nickname, Peng-Peng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peng-Peng Context triple: [Peng-Peng Lee, nickname, Peng-Peng]
-
A.
Madame Peng
Madame Peng is the honorific name commonly used for Peng Liyuan, a renowned Chinese soprano and the wife of Chinese leader Xi Jinping.
-
B.
Shaoqi
Shaoqi is the given name of Liu Shaoqi, a prominent Chinese revolutionary leader and former President of the People’s Republic of China.
-
C.
Yinping
Yinping was a strategically important mountainous region in ancient China, known as a key battleground during the late Three Kingdoms period.
-
D.
Jingjing
Jingjing is one of the five Fuwa mascots of the 2008 Beijing Summer Olympics, represented as a giant panda symbolizing happiness and prosperity.
-
E.
Mei Mei
Mei Mei is a confident and ribbon-dancing panda who serves as a prominent new character and love interest in the animated film "Kung Fu Panda 3."
- 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: Peng-Peng Triple: [Peng-Peng Lee, nickname, Peng-Peng]
Generated description
Peng-Peng is the nickname of Peng-Peng Lee, a Canadian-American artistic gymnast known for her elite career and standout NCAA performances with UCLA.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Peng-Peng Target entity description: Peng-Peng is the nickname of Peng-Peng Lee, a Canadian-American artistic gymnast known for her elite career and standout NCAA performances with UCLA.
-
A.
Madame Peng
Madame Peng is the honorific name commonly used for Peng Liyuan, a renowned Chinese soprano and the wife of Chinese leader Xi Jinping.
-
B.
Shaoqi
Shaoqi is the given name of Liu Shaoqi, a prominent Chinese revolutionary leader and former President of the People’s Republic of China.
-
C.
Yinping
Yinping was a strategically important mountainous region in ancient China, known as a key battleground during the late Three Kingdoms period.
-
D.
Jingjing
Jingjing is one of the five Fuwa mascots of the 2008 Beijing Summer Olympics, represented as a giant panda symbolizing happiness and prosperity.
-
E.
Mei Mei
Mei Mei is a confident and ribbon-dancing panda who serves as a prominent new character and love interest in the animated film "Kung Fu Panda 3."
- 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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e04033303c8190a87b6384f68a6921 |
completed | April 16, 2026, 1:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d5111648190a61fc87170b0d93c |
completed | May 9, 2026, 1:57 p.m. |
| NEDg | Description generation | batch_69ff3f075bb881908c254137ca7c3f9f |
completed | May 9, 2026, 2:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff3f87f788819080eccae52b0df145 |
completed | May 9, 2026, 2:07 p.m. |
Created at: April 10, 2026, 4:02 a.m.