Triple

T214675
Position Surface form Disambiguated ID Type / Status
Subject Populous E4792 entity
Predicate hasOfficeIn P1268 FINISHED
Object Dubai E3323 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: Dubai | Statement: [Populous, hasOfficeIn, Dubai]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Dubai
Context triple: [Populous, hasOfficeIn, Dubai]
  • A. Dubai chosen
    Dubai is a major global city in the United Arab Emirates known for its rapid development, luxury tourism, and status as a regional business and financial hub.
  • B. Abu Dhabi
    Abu Dhabi is the capital and second-most populous city of the United Arab Emirates, known for its vast oil wealth, modern skyline, and role as a major political and economic center in the Arab world.
  • C. Doha
    Doha is the rapidly developing capital and largest city of Qatar, known for its modern skyline, cultural institutions, and role as a major political and economic center in the Arab world.
  • D. Dubai Media City
    Dubai Media City is a specialized free zone and business hub in Dubai that hosts regional and international media, advertising, and communications companies.
  • E. Riyadh
    Riyadh is the capital and largest city of Saudi Arabia, serving as a major political, economic, and cultural center in the Arab world.
  • 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_69a2575cb1dc8190a01ad332426dc339 completed Feb. 28, 2026, 2:47 a.m.
NER Named-entity recognition batch_69a25c4ca0c8819093f63c6371e2d140 completed Feb. 28, 2026, 3:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69a36730f0a88190aac3d2e796ee544f completed Feb. 28, 2026, 10:07 p.m.
Created at: Feb. 28, 2026, 2:52 a.m.