Triple
T3450800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SIGKDD Service Award |
E72788
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object |
Xindong Wu
Xindong Wu is a prominent computer scientist known for his influential contributions to data mining and knowledge discovery research.
|
E358236
|
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: Xindong Wu | Statement: [SIGKDD Service Award, notableRecipient, Xindong Wu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Xindong Wu Context triple: [SIGKDD Service Award, notableRecipient, Xindong Wu]
-
A.
Xiaodong Chen
Xiaodong Chen is a prominent materials scientist and nanotechnology researcher who serves as editor-in-chief of the journal ACS Nano.
-
B.
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.
-
C.
Yanluo Wang
Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
-
D.
Wei Liu
Wei Liu is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work on object detection.
-
E.
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.
- 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: Xindong Wu Triple: [SIGKDD Service Award, notableRecipient, Xindong Wu]
Generated description
Xindong Wu is a prominent computer scientist known for his influential contributions to data mining and knowledge discovery research.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Xindong Wu Target entity description: Xindong Wu is a prominent computer scientist known for his influential contributions to data mining and knowledge discovery research.
-
A.
Xiaodong Chen
Xiaodong Chen is a prominent materials scientist and nanotechnology researcher who serves as editor-in-chief of the journal ACS Nano.
-
B.
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.
-
C.
Yanluo Wang
Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
-
D.
Wei Liu
Wei Liu is a computer scientist and researcher known for his contributions to deep learning and computer vision, including influential work on object detection.
-
E.
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.
- 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_69ad85b05c848190b7a28ceec2bd7b74 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adba7324508190b07943cec3ecdb59 |
completed | March 8, 2026, 6:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b360eb7ad08190865e62228365d530 |
completed | March 13, 2026, 12:57 a.m. |
| NEDg | Description generation | batch_69b3616dec3881908a54fa6500f7efb0 |
completed | March 13, 2026, 12:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b36211b6b08190ac0cac646160495d |
completed | March 13, 2026, 1:02 a.m. |
Created at: March 8, 2026, 3:16 p.m.