Triple

T32312523
Position Surface form Disambiguated ID Type / Status
Subject Uzhhorod National University E825537 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Dentistry
The Faculty of Dentistry is a specialized academic unit of Uzhhorod National University that educates and trains future dental professionals and conducts research in dental science and oral health.
 E2001799 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 Dentistry | Statement: [Uzhhorod National University, hasFaculty, Faculty of Dentistry]
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 Dentistry
Triple: [Uzhhorod National University, hasFaculty, Faculty of Dentistry]
Generated description
The Faculty of Dentistry is a specialized academic unit of Uzhhorod National University that educates and trains future dental professionals and conducts research in dental science and oral health.

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_69f3491213b88190a57094d8697a7455 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bdb8755481908645da7ceef44e17 completed May 3, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_6a305717c4dc8190ab084323dc9469a8 completed June 15, 2026, 7:48 p.m.
NEDg Description generation batch_6a305b4b640c8190a8e9166c63a9de7b completed June 15, 2026, 8:06 p.m.
NED2 Entity disambiguation (via description) batch_6a305bda28e88190b451fd544cc157fb completed June 15, 2026, 8:08 p.m.
Created at: May 1, 2026, 12:46 a.m.