Triple

T5352258
Position Surface form Disambiguated ID Type / Status
Subject Kingdom of Hejaz E102603 entity
Predicate includedRegion P285 FINISHED
Object Taif E87275 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: Taif | Statement: [Kingdom of Hejaz, includedRegion, Taif]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Taif
Context triple: [Kingdom of Hejaz, includedRegion, Taif]
  • A. Taif chosen
    Taif is a city in western Saudi Arabia known for its cool climate, rose cultivation, and historical significance as a summer resort and cultural center.
  • B. Taihoku
    Taihoku was the Japanese colonial-era name for Taipei, which served as the administrative and political center of Taiwan under Japanese rule.
  • C. Te Kao
    Te Kao is a small rural community at the northern end of New Zealand’s North Island, known for its strong Māori heritage and proximity to Ninety Mile Beach.
  • D. Tiba
    Tiba is a modern planned city in Egypt’s Luxor Governorate, developed to accommodate population growth and support regional economic and urban expansion.
  • E. Miyazya
    Miyazya is one of the spring months in the Ethiopian calendar, roughly corresponding to April in the Gregorian calendar.
  • 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_69bd43d8f7248190b64c140734b5c9a8 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd861327288190b3f2720ce81e0de6 completed March 20, 2026, 5:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf21d836b081908a5fb4e73397fa44 completed March 21, 2026, 10:55 p.m.
Created at: March 20, 2026, 2:01 p.m.