Triple

T14486812
Position Surface form Disambiguated ID Type / Status
Subject Prince of Yan E359249 entity
Predicate titleInPinyin P41219 FINISHED
Object Yan Wang E218806 NE FINISHED

How this triple was built (3 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: Yan Wang | Statement: [Prince of Yan, titleInPinyin, Yan Wang]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yan Wang
Context triple: [Prince of Yan, titleInPinyin, Yan Wang]
  • A. Yanluo Wang chosen
    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. 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.
  • D. Geling Yan
    Geling Yan is a Chinese-American novelist and screenwriter known for her emotionally powerful works that often explore the human impact of war, political upheaval, and social change in modern Chinese history.
  • E. Tingye Li
    Tingye Li was a pioneering Chinese-American optical engineer and physicist renowned for his foundational contributions to laser and fiber-optic communications.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: titleInPinyin
Context triple: [Prince of Yan, titleInPinyin, Yan Wang]
  • A. titleInKanji
    Indicates that one entity is the title of another entity written specifically in Kanji characters.
  • B. hasChineseTitle
    Indicates that an entity possesses a title or name expressed in the Chinese language.
  • C. titleInLatinScript
    Indicates that the title of an entity is written or represented using a Latin-based writing system.
  • D. titleInKorean
    Indicates that an entity has a specific title expressed in the Korean language.
  • E. ChinesePinyin chosen
    Indicates that one entity is the Chinese pinyin (romanized phonetic transcription) representation of another entity.
  • F. None of above.

Provenance (4 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_69d8279740308190af9df93a3af8592e completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de924ee0f08190baf68318b41fa64d completed April 14, 2026, 7:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd64a925148190992101984895a20b completed May 8, 2026, 4:20 a.m.
PD Predicate disambiguation batch_69de5c487b4c819097803e58dca628a5 completed April 14, 2026, 3:24 p.m.
Created at: April 10, 2026, 1:20 a.m.