Triple

T26010217

Position	Surface form	Disambiguated ID	Type / Status
Subject	Al-Asmariya Islamic University	`E646875`	entity
Predicate	hasFaculty	`P141`	FINISHED
Object	Faculty of Sharia The Faculty of Sharia is an academic division specializing in Islamic law and jurisprudence within Al-Asmariya Islamic University.	`E1705869`	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: Faculty of Sharia | Statement: [Al-Asmariya Islamic University, hasFaculty, Faculty of Sharia]

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: Faculty of Sharia
Triple: [Al-Asmariya Islamic University, hasFaculty, Faculty of Sharia]

Generated description

The Faculty of Sharia is an academic division specializing in Islamic law and jurisprudence within Al-Asmariya Islamic University.

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_69e77e89d5848190b54352cdb74f6029`	completed	April 21, 2026, 1:41 p.m.
NER	Named-entity recognition	`batch_69f605b4fa288190981533f604eea508`	completed	May 2, 2026, 2:09 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a1107a307e481909fda17e16e35911e`	completed	May 23, 2026, 1:49 a.m.
NEDg	Description generation	`batch_6a11082bcf9c8190a80f0ed23b79a823`	completed	May 23, 2026, 1:51 a.m.
NED2	Entity disambiguation (via description)	`batch_6a110c2ac828819088a7a9feb579e6e6`	completed	May 23, 2026, 2:08 a.m.

Created at: April 22, 2026, 9:01 a.m.