Triple

T24282370
Position Surface form Disambiguated ID Type / Status
Subject Al-Iqnaʿ E605579 entity
Predicate author P4 FINISHED
Object Musa ibn Ahmad al-Hajjawi
Musa ibn Ahmad al-Hajjawi was a prominent 16th-century Hanbali jurist best known for his influential works in Islamic jurisprudence that became standard references in the Hanbali school.
 E1641116 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: Musa ibn Ahmad al-Hajjawi | Statement: [Al-Iqnaʿ, author, Musa ibn Ahmad al-Hajjawi]
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: Musa ibn Ahmad al-Hajjawi
Triple: [Al-Iqnaʿ, author, Musa ibn Ahmad al-Hajjawi]
Generated description
Musa ibn Ahmad al-Hajjawi was a prominent 16th-century Hanbali jurist best known for his influential works in Islamic jurisprudence that became standard references in the Hanbali school.

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_69e295480d0c8190846fc3c2e2da1d4c completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f28f52e57c8190ab73e4b2b6a9eafd completed April 29, 2026, 11:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0ff838c57081908f40b3745c282471 completed May 22, 2026, 6:31 a.m.
NEDg Description generation batch_6a0ff956f6e48190950c5bace85c9669 completed May 22, 2026, 6:36 a.m.
NED2 Entity disambiguation (via description) batch_6a0ff9feda34819084e79982606c3972 completed May 22, 2026, 6:38 a.m.
Created at: April 18, 2026, 12:08 a.m.