Triple

T31969961
Position Surface form Disambiguated ID Type / Status
Subject Umm al-Qura University E816284 entity
Predicate hasCampus P116 FINISHED
Object Al-Abdiyyah campus
Al-Abdiyyah campus is a major campus of Umm al-Qura University in Mecca, serving as one of its primary centers for academic and administrative activities.
 E1987411 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-Abdiyyah campus | Statement: [Umm al-Qura University, hasCampus, Al-Abdiyyah campus]
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-Abdiyyah campus
Triple: [Umm al-Qura University, hasCampus, Al-Abdiyyah campus]
Generated description
Al-Abdiyyah campus is a major campus of Umm al-Qura University in Mecca, serving as one of its primary centers for academic and administrative activities.

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_69f348f5ae5481909da0247869f51955 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6b307715881908825d891df5304e6 completed May 3, 2026, 2:29 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2eb143ee1c8190b897d354c7131fc3 completed June 14, 2026, 1:48 p.m.
NEDg Description generation batch_6a2ebe77a1388190b91dc391cfdbe704 completed June 14, 2026, 2:45 p.m.
NED2 Entity disambiguation (via description) batch_6a2ebf3ea88c81909351a0f17e43a1db completed June 14, 2026, 2:48 p.m.
Created at: May 1, 2026, 12:10 a.m.