Triple

T8045767
Position Surface form Disambiguated ID Type / Status
Subject Sima Guang E187546 entity
Predicate nativeName P15 FINISHED
Object 司馬光 E187546 NE FINISHED

How this triple was built (2 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: 司馬光 | Statement: [Sima Guang, nativeName, 司馬光]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: 司馬光
Context triple: [Sima Guang, nativeName, 司馬光]
  • A. Sima Guang chosen
    Sima Guang was an influential Song dynasty historian and statesman best known for compiling the monumental historical chronicle "Zizhi Tongjian."
  • B. Ouyang Xiu
    Ouyang Xiu was an influential Song dynasty statesman, historian, and literary figure renowned for his prose, poetry, and role in shaping classical Chinese literature and scholarship.
  • C. He Zizhen
    He Zizhen was a Chinese revolutionary and early Communist Party member best known as one of Mao Zedong’s wives and a participant in the Long March.
  • D. Ban Gu
    Ban Gu was a prominent 1st-century Chinese historian and scholar of the Eastern Han dynasty, best known for authoring one of China’s most important official dynastic histories.
  • E. Chao Cuo
    Chao Cuo was an influential early Han dynasty statesman and reformer whose policies and advice on centralization and frontier defense helped shape imperial governance in China.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca82b00cb48190b59a300f70e97bd7 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3f4d9ddc8190a7dcf85ed47ee6c3 completed March 31, 2026, 3:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc5711d2bc8190911f2cade7596be5 completed March 31, 2026, 11:21 p.m.
Created at: March 30, 2026, 5:24 p.m.