Triple

T25003033
Position Surface form Disambiguated ID Type / Status
Subject Islamic historiography E625767 entity
Predicate hasKeyFigure P810 FINISHED
Object al-Khaṭīb al-Baghdādī
Al-Khaṭīb al-Baghdādī was an 11th-century Muslim scholar and historian best known for his biographical and hadith works, particularly his monumental history of Baghdad.
 E1659163 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: al-Khaṭīb al-Baghdādī | Statement: [Islamic historiography, hasKeyFigure, al-Khaṭīb al-Baghdādī]
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: al-Khaṭīb al-Baghdādī
Triple: [Islamic historiography, hasKeyFigure, al-Khaṭīb al-Baghdādī]
Generated description
Al-Khaṭīb al-Baghdādī was an 11th-century Muslim scholar and historian best known for his biographical and hadith works, particularly his monumental history of Baghdad.

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_69e2ff26c50481908bc82e799c9e6587 completed April 18, 2026, 3:48 a.m.
NER Named-entity recognition batch_69f44b0d1ed48190bcde75a65c8f86a0 completed May 1, 2026, 6:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a10337130b081909d2786694d821e1a completed May 22, 2026, 10:44 a.m.
NEDg Description generation batch_6a103422072c8190949546db07c0b9bd completed May 22, 2026, 10:46 a.m.
NED2 Entity disambiguation (via description) batch_6a103516e0e88190898a8b019ff7e6e5 completed May 22, 2026, 10:51 a.m.
Created at: April 18, 2026, 6:05 a.m.