Triple
T20200884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Acta Materialia Gold Medal |
E493212
|
entity |
| Predicate | hasAwarded |
P2391
|
FINISHED |
| Object |
Z. Suo
Z. Suo is a prominent materials scientist and engineer known for his influential research in the mechanics of materials and soft matter.
|
E1418548
|
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: Z. Suo | Statement: [Acta Materialia Gold Medal, hasAwarded, Z. Suo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Z. Suo Context triple: [Acta Materialia Gold Medal, hasAwarded, Z. Suo]
-
A.
Xiaohua Zhai
Xiaohua Zhai is a computer vision researcher known for co-introducing the Vision Transformer (ViT) architecture that applies transformer models to image recognition tasks.
-
B.
Zu Jia
Zu Jia was a king of the Shang dynasty in ancient China, known for his reign following the influential ruler Wu Ding and for efforts to consolidate royal authority.
-
C.
Zhang Mo
Zhang Mo is a Chinese actor and film director, best known as the son of acclaimed filmmaker Zhang Yimou and for his roles in Chinese television dramas and films.
-
D.
Zhu Chen
Zhu Chen is a Chinese-born Qatari chess grandmaster and former Women's World Chess Champion.
-
E.
Shuicheng Yan
Shuicheng Yan is a computer vision and machine learning researcher known for his influential work in deep learning architectures and visual recognition.
- 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: Z. Suo Triple: [Acta Materialia Gold Medal, hasAwarded, Z. Suo]
Generated description
Z. Suo is a prominent materials scientist and engineer known for his influential research in the mechanics of materials and soft matter.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Z. Suo Target entity description: Z. Suo is a prominent materials scientist and engineer known for his influential research in the mechanics of materials and soft matter.
-
A.
Xiaohua Zhai
Xiaohua Zhai is a computer vision researcher known for co-introducing the Vision Transformer (ViT) architecture that applies transformer models to image recognition tasks.
-
B.
Zu Jia
Zu Jia was a king of the Shang dynasty in ancient China, known for his reign following the influential ruler Wu Ding and for efforts to consolidate royal authority.
-
C.
Zhang Mo
Zhang Mo is a Chinese actor and film director, best known as the son of acclaimed filmmaker Zhang Yimou and for his roles in Chinese television dramas and films.
-
D.
Zhu Chen
Zhu Chen is a Chinese-born Qatari chess grandmaster and former Women's World Chess Champion.
-
E.
Shuicheng Yan
Shuicheng Yan is a computer vision and machine learning researcher known for his influential work in deep learning architectures and visual recognition.
- 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66d8d01648190b1b3a6e03f0258d8 |
completed | April 20, 2026, 6:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a084b805fdc819099b11150c8f4e62d |
completed | May 16, 2026, 10:48 a.m. |
| NEDg | Description generation | batch_6a084c041b5c8190881a6d08c4afe42f |
completed | May 16, 2026, 10:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a084c7dee808190a0883f7f0c7873a9 |
completed | May 16, 2026, 10:52 a.m. |
Created at: April 11, 2026, 11:37 p.m.