Triple

T24038023
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Engineering, University of Waterloo E595288 entity
Predicate hasSchool P113 FINISHED
Object School of Computer Science
The School of Computer Science is the academic unit at the University of Waterloo responsible for education and research in computer science and related fields.
E1614893 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: School of Computer Science | Statement: [Faculty of Engineering, University of Waterloo, hasSchool, School of Computer Science]
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: School of Computer Science
Triple: [Faculty of Engineering, University of Waterloo, hasSchool, School of Computer Science]
Generated description
The School of Computer Science is the academic unit at the University of Waterloo responsible for education and research in computer science and related fields.

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_69e288bf45f08190a1b6ed8cd0b9e86b completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1d8d6ce7c8190a41b2d9b459881bf completed April 29, 2026, 10:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f7eb1cec881908f2c68deb363c924 completed May 21, 2026, 9:52 p.m.
NEDg Description generation batch_6a0f7f6edf7081908ac1045c372e6351 completed May 21, 2026, 9:55 p.m.
NED2 Entity disambiguation (via description) batch_6a0f801bcc9c81908bbb270d7e762c11 completed May 21, 2026, 9:58 p.m.
Created at: April 17, 2026, 9:57 p.m.