Triple

T28398068
Position Surface form Disambiguated ID Type / Status
Subject Qassim University E719332 entity
Predicate hasFaculty P141 FINISHED
Object College of Engineering
The College of Engineering at Qassim University is an academic division that offers undergraduate and graduate engineering programs, conducts research, and provides technical education and training in various engineering disciplines.
 E1816854 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: [Qassim 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: [Qassim University, hasFaculty, College of Engineering]
Generated description
The College of Engineering at Qassim University is an academic division that offers undergraduate and graduate engineering programs, conducts research, and provides technical education and training in various engineering 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_69eff6efd1b08190ae3cefd4f11388a2 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64cf1aa7c8190b146b26dd4291025 completed May 2, 2026, 7:13 p.m.
NED1 Entity disambiguation (via context triple) batch_6a16330676e08190a328a0ca67862381 completed May 26, 2026, 11:55 p.m.
NEDg Description generation batch_6a1633dd88848190bf73982c2ce00ffd completed May 26, 2026, 11:59 p.m.
NED2 Entity disambiguation (via description) batch_6a16365587e08190b93663807e950946 completed May 27, 2026, 12:09 a.m.
Created at: April 28, 2026, 1:18 a.m.