Triple

T23507986
Position Surface form Disambiguated ID Type / Status
Subject Qansuh al-Ghawri E572338 entity
Predicate title P38 FINISHED
Object Al-Malik al-Ashraf
Al-Malik al-Ashraf was the regnal title of the late Mamluk sultan Qansuh al-Ghawri, reflecting his status as a powerful ruler of the Burji (Circassian) Mamluk dynasty in Egypt and Syria.
E1615578 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-Malik al-Ashraf | Statement: [Qansuh al-Ghawri, title, Al-Malik al-Ashraf]
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-Malik al-Ashraf
Triple: [Qansuh al-Ghawri, title, Al-Malik al-Ashraf]
Generated description
Al-Malik al-Ashraf was the regnal title of the late Mamluk sultan Qansuh al-Ghawri, reflecting his status as a powerful ruler of the Burji (Circassian) Mamluk dynasty in Egypt and Syria.

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_69e245b5e4208190bac8a6509867e394 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a901c9908190a781e79fe8b96743 completed April 29, 2026, 6:45 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f961f79bc81909da33922b038452f completed May 21, 2026, 11:32 p.m.
NEDg Description generation batch_6a0f974fb2e08190a535a92ead622159 completed May 21, 2026, 11:37 p.m.
NED2 Entity disambiguation (via description) batch_6a0f980f03548190b8c37ec67a132a93 completed May 21, 2026, 11:41 p.m.
Created at: April 17, 2026, 6:07 p.m.