Triple

T4470150
Position Surface form Disambiguated ID Type / Status
Subject Dueling DQN E98474 entity
Predicate introducedBy P513 FINISHED
Object Ziyu Wang
Ziyu Wang is a machine learning researcher best known for co-developing the dueling deep Q-network (Dueling DQN) architecture in deep reinforcement learning.
 E441097 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: Ziyu Wang | Statement: [Dueling DQN, introducedBy, Ziyu Wang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ziyu Wang
Context triple: [Dueling DQN, introducedBy, Ziyu Wang]
  • A. Yanluo Wang
    Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
  • 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. Jun-Yan Zhu
    Jun-Yan Zhu is a computer scientist and researcher known for his influential work in computer vision and generative models, particularly in image-to-image translation.
  • D. Zhong-Ying Wang
    Zhong-Ying Wang is a physicist known for collaborative work in theoretical and cosmological physics, including research conducted with Paul Steinhardt.
  • E. Xiangyu Zhang
    Xiangyu Zhang is a computer vision and deep learning researcher known for his contributions to convolutional neural network architectures and large-scale 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: Ziyu Wang
Triple: [Dueling DQN, introducedBy, Ziyu Wang]
Generated description
Ziyu Wang is a machine learning researcher best known for co-developing the dueling deep Q-network (Dueling DQN) architecture in deep reinforcement learning.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ziyu Wang
Target entity description: Ziyu Wang is a machine learning researcher best known for co-developing the dueling deep Q-network (Dueling DQN) architecture in deep reinforcement learning.
  • A. Yanluo Wang
    Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
  • 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. Jun-Yan Zhu
    Jun-Yan Zhu is a computer scientist and researcher known for his influential work in computer vision and generative models, particularly in image-to-image translation.
  • D. Zhong-Ying Wang
    Zhong-Ying Wang is a physicist known for collaborative work in theoretical and cosmological physics, including research conducted with Paul Steinhardt.
  • E. Xiangyu Zhang
    Xiangyu Zhang is a computer vision and deep learning researcher known for his contributions to convolutional neural network architectures and large-scale 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_69b3454b4ae481908967426dd37284d6 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3569cd03c8190927c596bedb45ac8 completed March 13, 2026, 12:13 a.m.
NED1 Entity disambiguation (via context triple) batch_69b6286c75b08190bd683d300f6c97f0 completed March 15, 2026, 3:33 a.m.
NEDg Description generation batch_69b6295627848190a7bb6b8943b0e3f1 completed March 15, 2026, 3:36 a.m.
NED2 Entity disambiguation (via description) batch_69b629be765c81908c1f6ccfc75604d1 completed March 15, 2026, 3:38 a.m.
Created at: March 12, 2026, 11:34 p.m.