Triple

T8021097
Position Surface form Disambiguated ID Type / Status
Subject Li Shizhen E186742 entity
Predicate artName P32318 FINISHED
Object Binhu
Binhu is the art name (courtesy name) of Li Shizhen, the renowned Ming dynasty physician and naturalist best known for compiling the monumental herbal pharmacopeia "Compendium of Materia Medica."
 E708806 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: Binhu | Statement: [Li Shizhen, artName, Binhu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Binhu
Context triple: [Li Shizhen, artName, Binhu]
  • A. Bingchang
    Bingchang is a Chinese given name, notably borne by diplomat and politician Fu Bingchang.
  • B. Paihuano
    Paihuano is a small town and commune in Chile’s Elqui Valley, known for its clear skies, pisco production, and astrotourism.
  • C. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • D. Dayong
    Dayong is the former name of the city now known as Zhangjiajie in Hunan Province, China, famed for its dramatic sandstone pillar landscapes.
  • E. Bocheng
    Bocheng is a Chinese given name most notably borne by the prominent Communist military leader and strategist Liu Bocheng.
  • 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: Binhu
Triple: [Li Shizhen, artName, Binhu]
Generated description
Binhu is the art name (courtesy name) of Li Shizhen, the renowned Ming dynasty physician and naturalist best known for compiling the monumental herbal pharmacopeia "Compendium of Materia Medica."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Binhu
Target entity description: Binhu is the art name (courtesy name) of Li Shizhen, the renowned Ming dynasty physician and naturalist best known for compiling the monumental herbal pharmacopeia "Compendium of Materia Medica."
  • A. Bingchang
    Bingchang is a Chinese given name, notably borne by diplomat and politician Fu Bingchang.
  • B. Paihuano
    Paihuano is a small town and commune in Chile’s Elqui Valley, known for its clear skies, pisco production, and astrotourism.
  • C. Guanggu
    Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
  • D. Dayong
    Dayong is the former name of the city now known as Zhangjiajie in Hunan Province, China, famed for its dramatic sandstone pillar landscapes.
  • E. Bocheng
    Bocheng is a Chinese given name most notably borne by the prominent Communist military leader and strategist Liu Bocheng.
  • 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_69ca82ac7fc081909b1398cf025423af completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3e8d90488190b57d1e748e272061 completed March 31, 2026, 3:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc56c82824819082e93eddc40bfad1 completed March 31, 2026, 11:20 p.m.
NEDg Description generation batch_69cc5ca6efbc819082f4c643446da354 completed March 31, 2026, 11:45 p.m.
NED2 Entity disambiguation (via description) batch_69cc5d6d93f08190b17d6c7a4fad2cf0 completed March 31, 2026, 11:49 p.m.
Created at: March 30, 2026, 5:20 p.m.