Triple

T1629358
Position Surface form Disambiguated ID Type / Status
Subject Lingnan E35222 entity
Predicate hasPart P35 FINISHED
Object Hainan E37179 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: Hainan | Statement: [Lingnan, hasPart, Hainan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hainan
Context triple: [Lingnan, hasPart, Hainan]
  • A. Hainan chosen
    Hainan is a tropical island province in southern China known for its beaches, tourism, and status as a major special economic zone.
  • B. Fujian
    Fujian is a coastal province in southeastern China known for its significant role in Chinese migration, distinctive Min culture and dialects, and historic maritime trade.
  • C. Taiwan, Province of China
    Taiwan, Province of China is a self-governed island territory in East Asia known for its advanced economy, vibrant democracy, and complex political status in relation to the People's Republic of China.
  • D. Guangdong Province
    Guangdong Province is a populous and economically vital coastal region in southern China, known for major cities like Guangzhou and Shenzhen and its role as a manufacturing and trade hub.
  • E. Guizhou Province
    Guizhou Province is a mountainous, ethnically diverse region in southwest China known for its karst landscapes, cool climate, and rapid economic development.
  • 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_69a886036bc081909ff5de16dbe5e8ea completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a909f257948190b3398fd6dc91f586 completed March 5, 2026, 4:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad58d5acd8819090c51678ce0f63f0 completed March 8, 2026, 11:09 a.m.
Created at: March 4, 2026, 7:28 p.m.