Triple

T656489
Position Surface form Disambiguated ID Type / Status
Subject Modern Standard Arabic E11660 entity
Predicate alsoKnownAs P39 FINISHED
Object Fus’ha
Fus’ha is the standardized, formal variety of Arabic used in writing, education, media, and official communication across the Arab world.
 E82099 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: Fus’ha | Statement: [Modern Standard Arabic, alsoKnownAs, Fus’ha]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Fus’ha
Context triple: [Modern Standard Arabic, alsoKnownAs, Fus’ha]
  • A. Seraiki
    Seraiki is an Indo-Aryan language spoken primarily in central and southern Pakistan, especially in the southern Punjab region.
  • B. Faizi
    Faizi was a renowned 16th-century Persian-language poet and scholar who served as one of the prominent intellectuals in the Mughal emperor Akbar’s court.
  • C. Shinsen
    Shinsen is a neighborhood in Tokyo’s Shibuya ward known for its residential streets, local eateries, and proximity to the bustling Shibuya Station area.
  • D. Nuskhuri
    Nuskhuri is a medieval Georgian script primarily used in religious manuscripts and liturgical texts of the Georgian Orthodox Church.
  • E. Babel
    Babel is a 2006 multi-narrative drama film directed by Alejandro González Iñárritu that interweaves interconnected stories across several countries to explore themes of communication, misfortune, and cultural misunderstanding.
  • 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: Fus’ha
Triple: [Modern Standard Arabic, alsoKnownAs, Fus’ha]
Generated description
Fus’ha is the standardized, formal variety of Arabic used in writing, education, media, and official communication across the Arab world.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Fus’ha
Target entity description: Fus’ha is the standardized, formal variety of Arabic used in writing, education, media, and official communication across the Arab world.
  • A. Seraiki
    Seraiki is an Indo-Aryan language spoken primarily in central and southern Pakistan, especially in the southern Punjab region.
  • B. Faizi
    Faizi was a renowned 16th-century Persian-language poet and scholar who served as one of the prominent intellectuals in the Mughal emperor Akbar’s court.
  • C. Shinsen
    Shinsen is a neighborhood in Tokyo’s Shibuya ward known for its residential streets, local eateries, and proximity to the bustling Shibuya Station area.
  • D. Nuskhuri
    Nuskhuri is a medieval Georgian script primarily used in religious manuscripts and liturgical texts of the Georgian Orthodox Church.
  • E. Babel
    Babel is a 2006 multi-narrative drama film directed by Alejandro González Iñárritu that interweaves interconnected stories across several countries to explore themes of communication, misfortune, and cultural misunderstanding.
  • 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_69a4932862a0819098be659c814e4981 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49f4e87408190b5276d2b913d0426 completed March 1, 2026, 8:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69a5914abe2c8190a27f520f445554d8 completed March 2, 2026, 1:31 p.m.
NEDg Description generation batch_69a5ab066d348190bbe5956cce0407ef completed March 2, 2026, 3:21 p.m.
NED2 Entity disambiguation (via description) batch_69a5c240eebc819098cd79447ed95b08 completed March 2, 2026, 5 p.m.
Created at: March 1, 2026, 7:36 p.m.