Triple

T25598231
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Economics, Universitas Negeri Malang E641714 entity
Predicate hasType P0 FINISHED
Object faculty of economics and business
The faculty of economics and business is an academic division specializing in education and research in economics, management, and related business disciplines.
E757220 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: faculty of economics and business | Statement: [Faculty of Economics, Universitas Negeri Malang, hasType, faculty of economics and business]
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: faculty of economics and business
Triple: [Faculty of Economics, Universitas Negeri Malang, hasType, faculty of economics and business]
Generated description
The faculty of economics and business is an academic division specializing in education and research in economics, management, and related business 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_69e75dc60d108190b7e2419e36b0134b completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f9a420f08190a8ed8c9a8c245fc4 completed May 2, 2026, 1:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10c13aac588190be4f51ed7c7dd9f9 completed May 22, 2026, 8:48 p.m.
NEDg Description generation batch_6a10c2a7e0b48190b17dd8b9bd8ccc0f completed May 22, 2026, 8:55 p.m.
NED2 Entity disambiguation (via description) batch_6a10c332191c81908f970d18fb2f37e9 completed May 22, 2026, 8:57 p.m.
Created at: April 21, 2026, 4:29 p.m.