Triple

T15833536
Position Surface form Disambiguated ID Type / Status
Subject Saudi Telecom Company E383928 entity
Predicate serviceArea P82 FINISHED
Object Saudi Arabia E6608 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: Saudi Arabia | Statement: [Saudi Telecom Company, serviceArea, Saudi Arabia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Saudi Arabia
Context triple: [Saudi Telecom Company, serviceArea, Saudi Arabia]
  • A. Saudi Arabia chosen
    Saudi Arabia is a Middle Eastern kingdom on the Arabian Peninsula known for its vast oil reserves, custodianship of Islam’s holiest sites, and significant geopolitical influence.
  • B. KSA
    KSA is the IATA airport code for Kosrae International Airport, which serves the island of Kosrae in the Federated States of Micronesia.
  • C. Sulaymaniya
    Sulaymaniya is a sub-school within the Zaydi branch of Shia Islam, distinguished by its own specific theological and legal interpretations.
  • D. Arabistan
    Arabistan is a region associated with Arab separatist aspirations, particularly within the context of movements seeking autonomy or independence from Iran.
  • E. Qatar
    Qatar is a wealthy Gulf nation on the Arabian Peninsula known for its vast natural gas reserves, rapid modernization, and large expatriate workforce.
  • 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_69d86da34c888190976e06c4019d415a completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e11e6670d48190a456581dd951f168 completed April 16, 2026, 5:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffa12d6f388190b61cdc7820ce6311 completed May 9, 2026, 9:03 p.m.
Created at: April 10, 2026, 4:49 a.m.