Triple

T6327058
Position Surface form Disambiguated ID Type / Status
Subject Commander of the Royal Saudi Naval Forces E141886 entity
Predicate seat P75 FINISHED
Object Riyadh E7632 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: Riyadh | Statement: [Commander of the Royal Saudi Naval Forces, seat, Riyadh]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Riyadh
Context triple: [Commander of the Royal Saudi Naval Forces, seat, Riyadh]
  • A. Riyadh chosen
    Riyadh is the capital and largest city of Saudi Arabia, serving as a major political, economic, and cultural center in the Arab world.
  • B. Dammam
    Dammam is a major Saudi Arabian city and commercial hub on the eastern coast, serving as a key center for the country’s oil industry and maritime trade.
  • C. Jeddah
    Jeddah is a major Saudi Arabian port city on the Red Sea, known as the gateway to the holy cities of Mecca and Medina and a key commercial and cultural hub in the region.
  • D. Diriyah
    Diriyah is a historic town in Saudi Arabia that served as the original home of the Saudi royal family and the capital of the first Saudi state.
  • E. Dhahran
    Dhahran is a major city in Saudi Arabia’s Eastern Province known for its role as a center of the country’s oil industry and the headquarters of Saudi Aramco.
  • 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_69c008d201748190917e69c41ba3f978 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c064e833a0819096000b47e776949d completed March 22, 2026, 9:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c60410223081908c1cf3663d4b14c0 completed March 27, 2026, 4:14 a.m.
Created at: March 22, 2026, 4:29 p.m.