Triple

T10181222
Position Surface form Disambiguated ID Type / Status
Subject Mount Tur E236785 entity
Predicate hasNameInArabic P6450 FINISHED
Object طور
طور هو جبل الطور المذكور في القرآن الكريم، والمرتبط بقصة النبي موسى عليه السلام وتلقيه الوحي.
 E845735 NE FINISHED

How this triple was built (4 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: طور | Statement: [Mount Tur, hasNameInArabic, طور]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: طور
Context triple: [Mount Tur, hasNameInArabic, طور]
  • A. Ben Turok
    Ben Turok was a South African anti-apartheid activist, economist, and long-serving parliamentarian known for his Marxist scholarship and leadership within the liberation movement.
  • B. Don
    Don is a masculine given name, often a short form of Donald, used in English-speaking countries.
  • C. Don
    Don is a classic 1978 Bollywood action-thriller film, starring Amitabh Bachchan in a dual role, that became iconic for its stylish crime narrative, memorable music, and enduring cultural impact.
  • D. Don
    The Don is a major river in southwestern Russia that flows from the Central Russian Upland to the Sea of Azov, historically serving as an important trade route and cultural boundary.
  • E. Kassar
    Kassar is a surname most notably associated with Mario F. Kassar, a prominent film producer known for major Hollywood action blockbusters.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: طور
Triple: [Mount Tur, hasNameInArabic, طور]
Generated description
طور هو جبل الطور المذكور في القرآن الكريم، والمرتبط بقصة النبي موسى عليه السلام وتلقيه الوحي.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: طور
Target entity description: طور هو جبل الطور المذكور في القرآن الكريم، والمرتبط بقصة النبي موسى عليه السلام وتلقيه الوحي.
  • A. Ben Turok
    Ben Turok was a South African anti-apartheid activist, economist, and long-serving parliamentarian known for his Marxist scholarship and leadership within the liberation movement.
  • B. Don
    The Don is a major river in southwestern Russia that flows from the Central Russian Upland to the Sea of Azov, historically serving as an important trade route and cultural boundary.
  • C. Don
    Don is a masculine given name, often a short form of Donald, used in English-speaking countries.
  • D. Don
    Don is a classic 1978 Bollywood action-thriller film, starring Amitabh Bachchan in a dual role, that became iconic for its stylish crime narrative, memorable music, and enduring cultural impact.
  • E. Kassar
    Kassar is a surname most notably associated with Mario F. Kassar, a prominent film producer known for major Hollywood action blockbusters.
  • F. None of above. chosen

Provenance (5 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_69ca84d7260c8190bfbec36762943f37 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cded315f14819085727bd9b4363d10 completed April 2, 2026, 4:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69d3013f0c54819092ee9c2c46fdf69e completed April 6, 2026, 12:41 a.m.
NEDg Description generation batch_69d3028a4384819094a7daef7287e54f completed April 6, 2026, 12:47 a.m.
NED2 Entity disambiguation (via description) batch_69d302e9d2688190bc292f8f2287c458 completed April 6, 2026, 12:48 a.m.
Created at: March 30, 2026, 9:11 p.m.