Triple

T24623215
Position Surface form Disambiguated ID Type / Status
Subject Ibrahim al-Nakhaʿi E609468 entity
Predicate taught P335 FINISHED
Object Hammad ibn Abi Sulayman
Hammad ibn Abi Sulayman was an early Kufan Islamic jurist and traditionist best known as the principal teacher of Abu Hanifa and a key figure in the development of the Hanafi school of law.
E617352 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: Hammad ibn Abi Sulayman | Statement: [Ibrahim al-Nakhaʿi, taught, Hammad ibn Abi Sulayman]
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: Hammad ibn Abi Sulayman
Triple: [Ibrahim al-Nakhaʿi, taught, Hammad ibn Abi Sulayman]
Generated description
Hammad ibn Abi Sulayman was an early Kufan Islamic jurist and traditionist best known as the principal teacher of Abu Hanifa and a key figure in the development of the Hanafi school of law.

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_69e2c4d1d3708190a0f2dc6a3a8523bb completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f2aa67c6a4819098d0960274d0b7bf completed April 30, 2026, 1:03 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10894f86ac8190a987b01da23d61f6 completed May 22, 2026, 4:50 p.m.
NEDg Description generation batch_6a108a0af25481909d520360b86ff170 completed May 22, 2026, 4:53 p.m.
NED2 Entity disambiguation (via description) batch_6a108afb4ad08190a1e9bcd731d98fcb completed May 22, 2026, 4:57 p.m.
Created at: April 18, 2026, 2:32 a.m.