Triple

T10547586
Position Surface form Disambiguated ID Type / Status
Subject سيبويه E248862 entity
Predicate kunya P26979 FINISHED
Object أبو بشر
أبو بشر هي كنية النحوي الشهير سيبويه، إمام مدرسة البصرة في النحو العربي ومؤلف كتاب "الكتاب" أحد أهم المراجع في تاريخ النحو.
 E871047 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: أبو بشر | Statement: [سيبويه, kunya, أبو بشر]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: أبو بشر
Context triple: [سيبويه, kunya, أبو بشر]
  • A. Abu al-Ula
    Abu al-Ula was a Muslim ruler in medieval Seville under whose authority the iconic Torre del Oro was constructed.
  • B. Abu Saʿid
    Abu Saʿid was an Ilkhanid ruler of Persia in the early 14th century, known for being the last effective sovereign of the Ilkhanate before its fragmentation.
  • C. Abu al-Zinad
    Abu al-Zinad was an early Islamic scholar and hadith transmitter known for his role in preserving and teaching prophetic traditions in Medina.
  • D. Abu Yaʿza Yalnour
    Abu Yaʿza Yalnour was an early Moroccan Sufi master and ascetic whose spiritual teachings and example deeply shaped the development of Maghrebi Sufism.
  • E. Abu al-Huda
    Abu al-Huda was a mid-20th-century Jordanian statesman who served multiple terms as prime minister under King Abdullah I and King Talal.
  • 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: أبو بشر
Triple: [سيبويه, kunya, أبو بشر]
Generated description
أبو بشر هي كنية النحوي الشهير سيبويه، إمام مدرسة البصرة في النحو العربي ومؤلف كتاب "الكتاب" أحد أهم المراجع في تاريخ النحو.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: أبو بشر
Target entity description: أبو بشر هي كنية النحوي الشهير سيبويه، إمام مدرسة البصرة في النحو العربي ومؤلف كتاب "الكتاب" أحد أهم المراجع في تاريخ النحو.
  • A. Abu al-Ula
    Abu al-Ula was a Muslim ruler in medieval Seville under whose authority the iconic Torre del Oro was constructed.
  • B. Abu Saʿid
    Abu Saʿid was an Ilkhanid ruler of Persia in the early 14th century, known for being the last effective sovereign of the Ilkhanate before its fragmentation.
  • C. Abu al-Zinad
    Abu al-Zinad was an early Islamic scholar and hadith transmitter known for his role in preserving and teaching prophetic traditions in Medina.
  • D. Abu Yaʿza Yalnour
    Abu Yaʿza Yalnour was an early Moroccan Sufi master and ascetic whose spiritual teachings and example deeply shaped the development of Maghrebi Sufism.
  • E. Abu al-Huda
    Abu al-Huda was a mid-20th-century Jordanian statesman who served multiple terms as prime minister under King Abdullah I and King Talal.
  • 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_69d381c733c08190ab1dd6239f5f34ae completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d526d20ef48190ab9f70d4ce5f2a11 completed April 7, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69d93457af7c819090f576ae606c5849 completed April 10, 2026, 5:33 p.m.
NEDg Description generation batch_69d93802a4488190aa86ae209650d4e7 completed April 10, 2026, 5:48 p.m.
NED2 Entity disambiguation (via description) batch_69d938fcc3c48190a4acaaf75c1aa304 completed April 10, 2026, 5:53 p.m.
Created at: April 6, 2026, 12:33 p.m.