Triple

T7986000
Position Surface form Disambiguated ID Type / Status
Subject Google File System E185682 entity
Predicate paperAuthors P2002 FINISHED
Object Shun-Tak Leung
Shun-Tak Leung is a computer scientist known for co-authoring the influential Google File System paper on distributed storage infrastructure at Google.
 E707879 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: Shun-Tak Leung | Statement: [Google File System, paperAuthors, Shun-Tak Leung]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Shun-Tak Leung
Context triple: [Google File System, paperAuthors, Shun-Tak Leung]
  • A. Chan Kwong-wing
    Chan Kwong-wing is a Hong Kong film composer best known for his scores for acclaimed movies such as the Infernal Affairs trilogy.
  • B. Ronald Cheng
    Ronald Cheng is a Hong Kong actor and Cantopop singer known for his comedic film roles and successful music career.
  • C. Norman Chan
    Norman Chan is a Hong Kong banker and civil servant best known for serving as Chief Executive of the Hong Kong Monetary Authority.
  • D. Ben Ngai-Cheung Lee
    Ben Ngai-Cheung Lee is a screenwriter known for his work in film and television, contributing to contemporary scripted storytelling.
  • E. Lai-Sang Young
    Lai-Sang Young is a prominent mathematician known for her influential work in dynamical systems and ergodic theory.
  • 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: Shun-Tak Leung
Triple: [Google File System, paperAuthors, Shun-Tak Leung]
Generated description
Shun-Tak Leung is a computer scientist known for co-authoring the influential Google File System paper on distributed storage infrastructure at Google.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Shun-Tak Leung
Target entity description: Shun-Tak Leung is a computer scientist known for co-authoring the influential Google File System paper on distributed storage infrastructure at Google.
  • A. Chan Kwong-wing
    Chan Kwong-wing is a Hong Kong film composer best known for his scores for acclaimed movies such as the Infernal Affairs trilogy.
  • B. Ronald Cheng
    Ronald Cheng is a Hong Kong actor and Cantopop singer known for his comedic film roles and successful music career.
  • C. Norman Chan
    Norman Chan is a Hong Kong banker and civil servant best known for serving as Chief Executive of the Hong Kong Monetary Authority.
  • D. Ben Ngai-Cheung Lee
    Ben Ngai-Cheung Lee is a screenwriter known for his work in film and television, contributing to contemporary scripted storytelling.
  • E. Lai-Sang Young
    Lai-Sang Young is a prominent mathematician known for her influential work in dynamical systems and ergodic theory.
  • 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_69ca829a2cfc819083d591d58ec04075 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c4b87e48190a797f5363c8f0a04 completed March 31, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc567cfe548190bbd163a32c340bcc completed March 31, 2026, 11:19 p.m.
NEDg Description generation batch_69cc58a8f3d08190bec84dc1ba3b5b84 completed March 31, 2026, 11:28 p.m.
NED2 Entity disambiguation (via description) batch_69cc5cb2963c81908a8dfbb1f84845bc completed March 31, 2026, 11:45 p.m.
Created at: March 30, 2026, 5:15 p.m.