Triple

T25475180

Position	Surface form	Disambiguated ID	Type / Status
Subject	Alabaster Mosque	`E638411`	entity
Predicate	alsoKnownAs	`P39`	FINISHED
Object	Muhammad Ali Mosque The Muhammad Ali Mosque is a prominent 19th-century Ottoman-style mosque and major landmark within Cairo's Citadel, known for its grand domes and extensive use of alabaster.	`E1684667`	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: Muhammad Ali Mosque | Statement: [Alabaster Mosque, alsoKnownAs, Muhammad Ali Mosque]

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: Muhammad Ali Mosque
Triple: [Alabaster Mosque, alsoKnownAs, Muhammad Ali Mosque]

Generated description

The Muhammad Ali Mosque is a prominent 19th-century Ottoman-style mosque and major landmark within Cairo's Citadel, known for its grand domes and extensive use of alabaster.

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_69e75db9b964819096802dcf502e577e`	completed	April 21, 2026, 11:21 a.m.
NER	Named-entity recognition	`batch_69f5f754447c8190acc16c440f8bb03d`	completed	May 2, 2026, 1:08 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a10ad64cd1081909c9e2adefbfc50d1`	completed	May 22, 2026, 7:24 p.m.
NEDg	Description generation	`batch_6a10aee5901481909b3c30231107e3cc`	completed	May 22, 2026, 7:30 p.m.
NED2	Entity disambiguation (via description)	`batch_6a10af914a4481909fad4723d5975df5`	completed	May 22, 2026, 7:33 p.m.

Created at: April 21, 2026, 2:25 p.m.