Triple

T311851
Position Surface form Disambiguated ID Type / Status
Subject Naskh script E7624 entity
Predicate standardizedBy P1371 FINISHED
Object Ibn Muqla
Ibn Muqla was a 10th-century Abbasid vizier and master calligrapher renowned for codifying the proportional rules that shaped classical Arabic scripts, especially Naskh.
 E40305 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: Ibn Muqla | Statement: [Naskh script, standardizedBy, Ibn Muqla]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ibn Muqla
Context triple: [Naskh script, standardizedBy, Ibn Muqla]
  • A. Ibrahim ibn Muhammad
    Ibrahim ibn Muhammad was the youngest son of the Prophet Muhammad, born in Medina and known for dying in infancy.
  • B. Qasim ibn Muhammad
    Qasim ibn Muhammad was the eldest son of the Islamic prophet Muhammad, who died in childhood in Mecca.
  • C. Al-Khalil
    Al-Khalil is the Arabic name for the city of Hebron in the West Bank, a historically significant and religiously important city revered in Islam, Judaism, and Christianity.
  • D. Abdullah ibn Muhammad
    Abdullah ibn Muhammad was one of the sons of the Islamic prophet Muhammad who died in early childhood.
  • E. Achmed Abdullah
    Achmed Abdullah was a Russian-born American writer and screenwriter known for his exotic adventure tales and contributions to early Hollywood cinema.
  • 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: Ibn Muqla
Triple: [Naskh script, standardizedBy, Ibn Muqla]
Generated description
Ibn Muqla was a 10th-century Abbasid vizier and master calligrapher renowned for codifying the proportional rules that shaped classical Arabic scripts, especially Naskh.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ibn Muqla
Target entity description: Ibn Muqla was a 10th-century Abbasid vizier and master calligrapher renowned for codifying the proportional rules that shaped classical Arabic scripts, especially Naskh.
  • A. Ibrahim ibn Muhammad
    Ibrahim ibn Muhammad was the youngest son of the Prophet Muhammad, born in Medina and known for dying in infancy.
  • B. Qasim ibn Muhammad
    Qasim ibn Muhammad was the eldest son of the Islamic prophet Muhammad, who died in childhood in Mecca.
  • C. Al-Khalil
    Al-Khalil is the Arabic name for the city of Hebron in the West Bank, a historically significant and religiously important city revered in Islam, Judaism, and Christianity.
  • D. Abdullah ibn Muhammad
    Abdullah ibn Muhammad was one of the sons of the Islamic prophet Muhammad who died in early childhood.
  • E. Achmed Abdullah
    Achmed Abdullah was a Russian-born American writer and screenwriter known for his exotic adventure tales and contributions to early Hollywood cinema.
  • 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_69a2e7e7af7881908890039d6be4e9b8 completed Feb. 28, 2026, 1:04 p.m.
NER Named-entity recognition batch_69a2ea49636c8190a69cbd951fc0a4db completed Feb. 28, 2026, 1:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69a3bc277528819096184b6cc6b98ae2 completed March 1, 2026, 4:10 a.m.
NEDg Description generation batch_69a3bea16ee48190a033ea3907703e40 completed March 1, 2026, 4:20 a.m.
NED2 Entity disambiguation (via description) batch_69a3befc80a8819092506dd29e18f2aa completed March 1, 2026, 4:22 a.m.
Created at: Feb. 28, 2026, 1:07 p.m.