Triple

T21137201
Position Surface form Disambiguated ID Type / Status
Subject Hanshu E520843 entity
Predicate author P4 FINISHED
Object Ban Gu NE NERFINISHED

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: Ban Gu | Statement: [Hanshu, author, Ban Gu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ban Gu
Context triple: [Hanshu, author, Ban Gu]
  • A. Ban Gu chosen
    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.
  • B. Liu Ban
    Liu Ban was a Chinese scholar and official known for serving on the editorial team that compiled the historical chronicle Zizhi Tongjian.
  • C. Dong Zhongshu
    Dong Zhongshu was a prominent Han dynasty scholar and political philosopher who systematized Confucianism into an official state ideology, deeply shaping Chinese thought and governance.
  • D. Sima Qian
    Sima Qian was an eminent Chinese historian of the Former Han dynasty, best known for authoring the foundational historical text "Records of the Grand Historian" (Shiji).
  • E. Chang Ch’ün
    Chang Ch’ün (Zhang Qun) was a prominent 20th-century Chinese Nationalist politician and diplomat who served in key roles including premier and foreign minister of the Republic of China.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b50b53048190ae34e8abbe3c5ada completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e7235b89188190a6209c0a1839ee03 completed April 21, 2026, 7:12 a.m.
Created at: April 16, 2026, 2:57 p.m.