Triple
T3450798
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SIGKDD Service Award |
E72788
|
entity |
| Predicate | notableRecipient |
P108
|
FINISHED |
| Object | Jiawei Han |
E356897
|
NE FINISHED |
How this triple was built (2 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 Service Award, notableRecipient, Jiawei Han]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jiawei Han Context triple: [SIGKDD Service Award, notableRecipient, Jiawei Han]
-
A.
Jiawei Han
chosen
Jiawei Han is a prominent computer scientist renowned for his pioneering contributions to data mining and knowledge discovery.
-
B.
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.
-
C.
Xiaodong Chen
Xiaodong Chen is a prominent materials scientist and nanotechnology researcher who serves as editor-in-chief of the journal ACS Nano.
-
D.
Philip S. Yu
Philip S. Yu is a prominent computer scientist known for his influential contributions to data mining, databases, and big data analytics.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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. |
Created at: March 8, 2026, 3:16 p.m.