Triple

T31201158
Position Surface form Disambiguated ID Type / Status
Subject Hanseo University E795475 entity
Predicate hasAcademicUnit P1488 FINISHED
Object College of Aviation
The College of Aviation is an academic unit of Hanseo University specializing in aviation-related education and training for careers in the aerospace and airline industries.
 E1950794 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 Aviation | Statement: [Hanseo University, hasAcademicUnit, College of Aviation]
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 Aviation
Triple: [Hanseo University, hasAcademicUnit, College of Aviation]
Generated description
The College of Aviation is an academic unit of Hanseo University specializing in aviation-related education and training for careers in the aerospace and airline industries.

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_69f224d8c6608190b7882466521f62be completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69bc2ca808190876e5cb05012dbfc completed May 3, 2026, 12:50 a.m.
NED1 Entity disambiguation (via context triple) batch_6a29591e7ab48190ac54ad3e501b9b83 completed June 10, 2026, 12:31 p.m.
NEDg Description generation batch_6a295a14ff9481909756485f202d3a58 completed June 10, 2026, 12:35 p.m.
NED2 Entity disambiguation (via description) batch_6a295a8606948190ad52f1240742a2ca completed June 10, 2026, 12:37 p.m.
Created at: April 29, 2026, 9:09 p.m.