Triple
T21570301
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Playdom |
E532264
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Michael Li
Michael Li is an entrepreneur best known as a co-founder of Playdom, a social gaming company that became a major developer of games for social networks.
|
E1492630
|
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: Michael Li | Statement: [Playdom, foundedBy, Michael Li]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Li Context triple: [Playdom, foundedBy, Michael Li]
-
A.
William Li
William Li is a Chinese entrepreneur best known as the founder and CEO of the electric vehicle company NIO.
-
B.
Michael Lai
Michael Lai was a prominent Hong Kong composer and music producer best known for his influential film and television scores.
-
C.
Michael Yu
Michael Yu is a prominent Chinese entrepreneur and educator best known as the founder of New Oriental Education & Technology Group, one of China’s largest private education providers.
-
D.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
-
E.
Michael Wong
Michael Wong is a Hong Kong-based actor and singer known for his roles in action and crime films across Asian cinema.
- 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: Michael Li Triple: [Playdom, foundedBy, Michael Li]
Generated description
Michael Li is an entrepreneur best known as a co-founder of Playdom, a social gaming company that became a major developer of games for social networks.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Michael Li Target entity description: Michael Li is an entrepreneur best known as a co-founder of Playdom, a social gaming company that became a major developer of games for social networks.
-
A.
William Li
William Li is a Chinese entrepreneur best known as the founder and CEO of the electric vehicle company NIO.
-
B.
Michael Lai
Michael Lai was a prominent Hong Kong composer and music producer best known for his influential film and television scores.
-
C.
Michael Yu
Michael Yu is a prominent Chinese entrepreneur and educator best known as the founder of New Oriental Education & Technology Group, one of China’s largest private education providers.
-
D.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
-
E.
Michael Wong
Michael Wong is a Hong Kong-based actor and singer known for his roles in action and crime films across Asian cinema.
- 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_69e0c460db088190828c64206a450273 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eee9cc5c448190887a836f577d01ba |
completed | April 27, 2026, 4:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09fd2805508190af5e426389ec756b |
completed | May 17, 2026, 5:38 p.m. |
| NEDg | Description generation | batch_6a09fe2d2a688190ac396350643fcb72 |
completed | May 17, 2026, 5:43 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09fe8d67048190917d4c1d6e5b81f2 |
completed | May 17, 2026, 5:44 p.m. |
Created at: April 16, 2026, 6:30 p.m.