Triple

T3317643
Position Surface form Disambiguated ID Type / Status
Subject Qatar Executive E69718 entity
Predicate country P26 FINISHED
Object Qatar E13453 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: Qatar | Statement: [Qatar Executive, country, Qatar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Qatar
Context triple: [Qatar Executive, country, Qatar]
  • A. Qatar chosen
    Qatar is a wealthy Gulf nation on the Arabian Peninsula known for its vast natural gas reserves, rapid modernization, and large expatriate workforce.
  • B. Kuwait
    Kuwait is a small, oil-rich Gulf nation on the Arabian Peninsula known for its modern capital Kuwait City, significant expatriate workforce, and strategic geopolitical importance.
  • C. Bahrain
    Bahrain is a small island nation in the Persian Gulf known for its rich history, oil wealth, and status as a regional financial and cultural hub.
  • D. Saudi Arabia
    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.
  • E. United Arab Emirates
    The United Arab Emirates is a wealthy Gulf nation on the Arabian Peninsula known for its rapid modernization, iconic cities like Dubai and Abu Dhabi, and its 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_69ad85a0bb048190a5458d2738012d61 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb113cb6c8190989b06476f6015fd completed March 8, 2026, 5:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f394df3c819087cd773980b42f2e completed March 12, 2026, 5:10 p.m.
Created at: March 8, 2026, 3:11 p.m.