Triple
T3425606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SIGKDD Innovation Award |
E72222
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object |
Jiawei Han
Jiawei Han is a prominent computer scientist renowned for his pioneering contributions to data mining and knowledge discovery.
|
E356897
|
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: Jiawei Han | Statement: [SIGKDD Innovation Award, notableRecipient, Jiawei Han]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jiawei Han Context triple: [SIGKDD Innovation Award, notableRecipient, Jiawei Han]
-
A.
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.
-
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.
Quoc V. Le
Quoc V. Le is a prominent computer scientist and AI researcher known for his influential work in deep learning and large-scale machine learning at Google.
-
D.
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.
-
E.
Andrew T. Hsu
Andrew T. Hsu is an academic leader and engineer who serves as the president of the College of Charleston.
- 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: Jiawei Han Triple: [SIGKDD Innovation Award, notableRecipient, Jiawei Han]
Generated description
Jiawei Han is a prominent computer scientist renowned for his pioneering contributions to data mining and knowledge discovery.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jiawei Han Target entity description: Jiawei Han is a prominent computer scientist renowned for his pioneering contributions to data mining and knowledge discovery.
-
A.
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.
-
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.
Quoc V. Le
Quoc V. Le is a prominent computer scientist and AI researcher known for his influential work in deep learning and large-scale machine learning at Google.
-
D.
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.
-
E.
Andrew T. Hsu
Andrew T. Hsu is an academic leader and engineer who serves as the president of the College of Charleston.
- 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_69ad85ae14308190bcbc25cfa0246c0b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb9812a648190ac919e7291744b5a |
completed | March 8, 2026, 6:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b354766bcc81909fb3124262c93f8f |
completed | March 13, 2026, 12:04 a.m. |
| NEDg | Description generation | batch_69b35565a688819096c7e5fdf8e944bf |
completed | March 13, 2026, 12:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b355efa89c8190bf9b2eb3c41257b3 |
completed | March 13, 2026, 12:10 a.m. |
Created at: March 8, 2026, 3:15 p.m.