Triple
T1525944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Crystal Award |
E32335
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object |
Yao Chen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
|
E174332
|
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: Yao Chen | Statement: [Crystal Award, notableRecipient, Yao Chen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yao Chen Context triple: [Crystal Award, notableRecipient, Yao Chen]
-
A.
Yuhuai Wu
Yuhuai Wu is an AI researcher and entrepreneur known for his work on large language models and as a member of Elon Musk’s xAI team.
-
B.
Xiaodong Chen
Xiaodong Chen is a prominent materials scientist and nanotechnology researcher who serves as editor-in-chief of the journal ACS Nano.
-
C.
Younan Xia
Younan Xia is a prominent chemist and materials scientist known for his pioneering work in nanomaterials synthesis and nanotechnology.
-
D.
Langche Zeng
Langche Zeng is a political scientist and quantitative methodologist known for his collaborative work with Gary King on statistical methods in social science research.
-
E.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
- 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: Yao Chen Triple: [Crystal Award, notableRecipient, Yao Chen]
Generated description
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yao Chen Target entity description: Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
A.
Yuhuai Wu
Yuhuai Wu is an AI researcher and entrepreneur known for his work on large language models and as a member of Elon Musk’s xAI team.
-
B.
Xiaodong Chen
Xiaodong Chen is a prominent materials scientist and nanotechnology researcher who serves as editor-in-chief of the journal ACS Nano.
-
C.
Younan Xia
Younan Xia is a prominent chemist and materials scientist known for his pioneering work in nanomaterials synthesis and nanotechnology.
-
D.
Langche Zeng
Langche Zeng is a political scientist and quantitative methodologist known for his collaborative work with Gary King on statistical methods in social science research.
-
E.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
- 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_69a885e9b0ac819093a9806ad0efc82c |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa61f7bb60819094774ecc632255de |
completed | March 6, 2026, 5:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad2953c5308190984d20f62b7303fd |
completed | March 8, 2026, 7:46 a.m. |
| NEDg | Description generation | batch_69ad2a1742d48190a82c1fc8c81d5c21 |
completed | March 8, 2026, 7:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad2aa092b08190930f1c39d963861b |
completed | March 8, 2026, 7:52 a.m. |
Created at: March 4, 2026, 7:26 p.m.