Triple

T30764001
Position Surface form Disambiguated ID Type / Status
Subject University of Rzeszów E783313 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Art
The Faculty of Art at the University of Rzeszów is an academic unit dedicated to education and research in the visual and performing arts.
 E1931307 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 Art | Statement: [University of Rzeszów, hasFaculty, Faculty of Art]
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 Art
Triple: [University of Rzeszów, hasFaculty, Faculty of Art]
Generated description
The Faculty of Art at the University of Rzeszów is an academic unit dedicated to education and research in the visual and performing arts.

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_69f224b047f48190b4f5efeb7ee97b37 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f68fbafbc4819095a36c8ccf608452 completed May 2, 2026, 11:58 p.m.
NED1 Entity disambiguation (via context triple) batch_6a28b09490a881908fea30b0878c7000 completed June 10, 2026, 12:32 a.m.
NEDg Description generation batch_6a28b2031d8c8190912ab58c5966ca52 completed June 10, 2026, 12:38 a.m.
NED2 Entity disambiguation (via description) batch_6a28b2cac6ec819095d1b4f927d9f772 completed June 10, 2026, 12:41 a.m.
Created at: April 29, 2026, 8:39 p.m.