Triple

T25089445
Position Surface form Disambiguated ID Type / Status
Subject Yuan Ze University E628413 entity
Predicate hasFaculty P141 FINISHED
Object College of Management
The College of Management at Yuan Ze University is an academic unit specializing in business and management education and research within the university.
 E1669746 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 Management | Statement: [Yuan Ze University, hasFaculty, College of Management]
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 Management
Triple: [Yuan Ze University, hasFaculty, College of Management]
Generated description
The College of Management at Yuan Ze University is an academic unit specializing in business and management education and research within the 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_69e2ff2f58e881908340527bc5d34f07 completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f461e7fe048190adfaf71724f707e3 completed May 1, 2026, 8:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1067b93c588190b477a4804e3fcbd6 completed May 22, 2026, 2:27 p.m.
NEDg Description generation batch_6a10685f5b448190912ca34fc1f71dbb completed May 22, 2026, 2:29 p.m.
NED2 Entity disambiguation (via description) batch_6a1068f5bebc81908c925d397df6fa77 completed May 22, 2026, 2:32 p.m.
Created at: April 18, 2026, 6:24 a.m.