Triple

T24480271
Position Surface form Disambiguated ID Type / Status
Subject Hammad ibn Abi Sulayman E617352 entity
Predicate teacher P335 FINISHED
Object Abu Wail Shaqiq ibn Salama
Abu Wail Shaqiq ibn Salama was an early Kufan Muslim scholar and reliable hadith transmitter counted among the Tabi'un (the generation after the Companions).
E1650758 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: Abu Wail Shaqiq ibn Salama | Statement: [Hammad ibn Abi Sulayman, teacher, Abu Wail Shaqiq ibn Salama]
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: Abu Wail Shaqiq ibn Salama
Triple: [Hammad ibn Abi Sulayman, teacher, Abu Wail Shaqiq ibn Salama]
Generated description
Abu Wail Shaqiq ibn Salama was an early Kufan Muslim scholar and reliable hadith transmitter counted among the Tabi'un (the generation after the Companions).

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_69e2d7f3ae788190b683394db15f220e completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f29ed509c88190a0071f8e78b38887 completed April 30, 2026, 12:14 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101bdc4dc4819081d422be8c0fc3be completed May 22, 2026, 9:03 a.m.
NEDg Description generation batch_6a10248751648190aabfa72ad8ab0b3f completed May 22, 2026, 9:40 a.m.
NED2 Entity disambiguation (via description) batch_6a102586c1288190bf8eeb513537b189 completed May 22, 2026, 9:44 a.m.
Created at: April 18, 2026, 2:21 a.m.