Triple

T27248255
Position Surface form Disambiguated ID Type / Status
Subject Kunsan National University E687409 entity
Predicate hasFaculty P141 FINISHED
Object College of Education
The College of Education at Kunsan National University is an academic unit dedicated to preparing future teachers and education professionals through specialized training and research in pedagogy and educational practice.
E1763916 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 Education | Statement: [Kunsan National University, hasFaculty, College of Education]
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 Education
Triple: [Kunsan National University, hasFaculty, College of Education]
Generated description
The College of Education at Kunsan National University is an academic unit dedicated to preparing future teachers and education professionals through specialized training and research in pedagogy and educational practice.

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_69ef355547408190b5ca0d777c65040a completed April 27, 2026, 10:07 a.m.
NER Named-entity recognition batch_69f626b3e5508190acb0c7b03e465b31 completed May 2, 2026, 4:30 p.m.
NED1 Entity disambiguation (via context triple) batch_6a126266844c8190a2d6506ae7b22111 completed May 24, 2026, 2:28 a.m.
NEDg Description generation batch_6a127290abac8190a03b9edff37bc1ea completed May 24, 2026, 3:37 a.m.
NED2 Entity disambiguation (via description) batch_6a12734919108190975bb8f61a5abe84 completed May 24, 2026, 3:40 a.m.
Created at: April 27, 2026, 10:43 a.m.