Triple

T30213697

Position	Surface form	Disambiguated ID	Type / Status
Subject	Al-Jami al-Kabir	`E768139`	entity
Predicate	category	`P87`	FINISHED
Object	Great Mosques Great Mosques are large, central congregational mosques that serve as primary places of worship and community gathering, especially for Friday prayers, in Islamic cities and towns.	`E1903476`	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: Great Mosques | Statement: [Al-Jami al-Kabir, category, Great Mosques]

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: Great Mosques
Triple: [Al-Jami al-Kabir, category, Great Mosques]

Generated description

Great Mosques are large, central congregational mosques that serve as primary places of worship and community gathering, especially for Friday prayers, in Islamic cities and towns.

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_69f2247fd8b8819087fcf83cb7a05eb8`	completed	April 29, 2026, 3:32 p.m.
NER	Named-entity recognition	`batch_69f67ff359688190bb36d178d11fdb04`	completed	May 2, 2026, 10:51 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a2758eb1ab88190981060de27a4fb20`	completed	June 9, 2026, 12:06 a.m.
NEDg	Description generation	`batch_6a275a8244788190837ad72957f937db`	completed	June 9, 2026, 12:12 a.m.
NED2	Entity disambiguation (via description)	`batch_6a275b94cf908190a828d9b24d444b01`	completed	June 9, 2026, 12:17 a.m.

Created at: April 29, 2026, 7:33 p.m.