Triple

T16748923
Position Surface form Disambiguated ID Type / Status
Subject SAGE Publications E407023 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: [SAGE Publications, hasOfficeIn, Shanghai]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shanghai
Context triple: [SAGE Publications, 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 an unincorporated community located in Berkeley County, West Virginia, United States.
  • C. 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.
  • D. Hangtou
    Hangtou is a town in Shanghai, China, known as the southern terminus of the Shanghai Metro’s Line 18.
  • E. Beijing
    Beijing is the capital city of China, a major political, cultural, and economic center known for its rich history and rapid modern 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_69d8838ffb088190a0b11149929006bf completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3aa2532ac81908e5ee5148e35f92e completed April 18, 2026, 3:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b281cce881908c401bca3cf21dfc completed May 10, 2026, 4:29 p.m.
Created at: April 10, 2026, 5:21 a.m.