Triple

T9667414
Position Surface form Disambiguated ID Type / Status
Subject Dongfang E233740 entity
Predicate locatedIn P40 FINISHED
Object Hainan Province 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 Province | Statement: [Dongfang, locatedIn, Hainan Province]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hainan Province
Context triple: [Dongfang, locatedIn, Hainan Province]
  • 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. 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.
  • C. 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.
  • D. Guizhou Province
    Guizhou Province is a mountainous, ethnically diverse region in southwest China known for its karst landscapes, cool climate, and rapid economic development.
  • E. Bo’ai Special Zone
    Bo’ai Special Zone is a prominent administrative and civic district in Taipei, Taiwan, known for concentrating key government institutions and public buildings.
  • 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_69ca848d3b6c8190ae98ea554dea58df completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9c3c06e4819080c1b8e66faa482f completed April 1, 2026, 10:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69d18a208798819088db055e44d288e3 completed April 4, 2026, 10:01 p.m.
Created at: March 30, 2026, 8:15 p.m.