Triple

T18204261
Position Surface form Disambiguated ID Type / Status
Subject RoBERTa E435864 entity
Predicate paperAuthorsInclude P63068 FINISHED
Object Yinhan Liu
Yinhan Liu is a natural language processing researcher best known for co-developing the RoBERTa language model and related advances in large-scale pretraining.
 E1312411 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: Yinhan Liu | Statement: [RoBERTa, paperAuthorsInclude, Yinhan Liu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Yinhan Liu
Context triple: [RoBERTa, paperAuthorsInclude, Yinhan Liu]
  • A. Huan Liu
    Huan Liu is a prominent computer scientist known for his influential research in data mining and machine learning, particularly in feature selection and social media analytics.
  • B. Tingye Li
    Tingye Li was a pioneering Chinese-American optical engineer and physicist renowned for his foundational contributions to laser and fiber-optic communications.
  • C. 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.
  • D. Yanluo Wang
    Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
  • E. 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.
  • 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: Yinhan Liu
Triple: [RoBERTa, paperAuthorsInclude, Yinhan Liu]
Generated description
Yinhan Liu is a natural language processing researcher best known for co-developing the RoBERTa language model and related advances in large-scale pretraining.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Yinhan Liu
Target entity description: Yinhan Liu is a natural language processing researcher best known for co-developing the RoBERTa language model and related advances in large-scale pretraining.
  • A. Huan Liu
    Huan Liu is a prominent computer scientist known for his influential research in data mining and machine learning, particularly in feature selection and social media analytics.
  • B. Tingye Li
    Tingye Li was a pioneering Chinese-American optical engineer and physicist renowned for his foundational contributions to laser and fiber-optic communications.
  • C. 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.
  • D. Yanluo Wang
    Yanluo Wang is the Chinese deity who presides over the underworld and judges the souls of the dead.
  • E. 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.
  • 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_69d8b90dba6481908e119eb9aa4ca0cb completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4e222831081908f7d5500424e3acb completed April 19, 2026, 2:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a039f0e52108190913cc5c667619d89 completed May 12, 2026, 9:43 p.m.
NEDg Description generation batch_6a039fdd9c4c819083b450657d0ece43 completed May 12, 2026, 9:47 p.m.
NED2 Entity disambiguation (via description) batch_6a03a0d6de8c8190b1f94c7de0856143 completed May 12, 2026, 9:51 p.m.
Created at: April 10, 2026, 10:32 a.m.