Triple

T25078191
Position Surface form Disambiguated ID Type / Status
Subject Chung Yuan Christian University E628110 entity
Predicate hasFaculty P141 FINISHED
Object College of Design
The College of Design is an academic division of Chung Yuan Christian University in Taiwan that focuses on education and research in design-related disciplines.
 E1664449 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 Design | Statement: [Chung Yuan Christian University, hasFaculty, College of Design]
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 Design
Triple: [Chung Yuan Christian University, hasFaculty, College of Design]
Generated description
The College of Design is an academic division of Chung Yuan Christian University in Taiwan that focuses on education and research in design-related 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_69e2ff2e73f881909992bf3eda5c25cb completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f45d1b9bf081909e1a639b63f13a1b completed May 1, 2026, 7:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048e1900c8190a6697997fcb25336 completed May 22, 2026, 12:15 p.m.
NEDg Description generation batch_6a104c1f2e448190b0ee1a8c0bca7520 completed May 22, 2026, 12:29 p.m.
NED2 Entity disambiguation (via description) batch_6a104d107b088190aad51efff7bcef1e completed May 22, 2026, 12:33 p.m.
Created at: April 18, 2026, 6:21 a.m.