Triple

T11067409
Position Surface form Disambiguated ID Type / Status
Subject Ateliers Jean Nouvel E261660 entity
Predicate hasOfficeIn P1268 FINISHED
Object Shanghai E5256 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: Shanghai | Statement: [Ateliers Jean Nouvel, hasOfficeIn, Shanghai]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shanghai
Context triple: [Ateliers Jean Nouvel, hasOfficeIn, Shanghai]
  • A. Shanghai chosen
    Shanghai is a major global financial hub and China’s largest city, known for its modern skyline, historic waterfront, and role as a center of international business and trade.
  • B. Shanghai
    Shanghai is a major Ethereum network upgrade that introduced key changes such as enabling staked ETH withdrawals and improving the protocol’s efficiency and flexibility.
  • C. Beijing
    Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern development.
  • D. Tianjin
    Tianjin is a major port city and industrial hub in northern China, located near Beijing along the Bohai Sea.
  • E. Guangzhou
    Guangzhou is a major port city in southern China and the capital of Guangdong Province, known as a key commercial and manufacturing hub in the Pearl River Delta.
  • 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_69d6aa98650481908609c7c56bfa7902 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d79920428c81908db824ab54e08e8d completed April 9, 2026, 12:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69e3c828b7848190be34a6ba1550d3f1 completed April 18, 2026, 6:06 p.m.
Created at: April 8, 2026, 9:26 p.m.