Triple

T32747525
Position Surface form Disambiguated ID Type / Status
Subject Shaqra University E837393 entity
Predicate hasFaculty P141 FINISHED
Object College of Engineering
The College of Engineering is an academic division of Shaqra University that offers engineering education and training across various technical disciplines.
 E2020830 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: College of Engineering | Statement: [Shaqra University, hasFaculty, College of Engineering]
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: College of Engineering
Triple: [Shaqra University, hasFaculty, College of Engineering]
Generated description
The College of Engineering is an academic division of Shaqra University that offers engineering education and training across various technical disciplines.

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_69f34936e1748190b797e406e4e9293a completed April 30, 2026, 12:21 p.m.
NER Named-entity recognition batch_69f6cc1fcf0881908c53879f48893952 completed May 3, 2026, 4:16 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34a7b5e5ec8190b15583ec64c2390a completed June 19, 2026, 2:21 a.m.
NEDg Description generation batch_6a34a84e9e3881909614d79de44dd3cc completed June 19, 2026, 2:24 a.m.
NED2 Entity disambiguation (via description) batch_6a34a8dde9f48190b9912c18f2470edf completed June 19, 2026, 2:26 a.m.
Created at: May 1, 2026, 1:12 a.m.