Triple

T26404909
Position Surface form Disambiguated ID Type / Status
Subject University of Salzburg E663804 entity
Predicate hasDepartment P35 FINISHED
Object Department of Geoinformatics
The Department of Geoinformatics at the University of Salzburg is an academic unit specializing in geographic information science, spatial analysis, and related geospatial technologies.
E1722396 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: Department of Geoinformatics | Statement: [University of Salzburg, hasDepartment, Department of Geoinformatics]
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: Department of Geoinformatics
Triple: [University of Salzburg, hasDepartment, Department of Geoinformatics]
Generated description
The Department of Geoinformatics at the University of Salzburg is an academic unit specializing in geographic information science, spatial analysis, and related geospatial technologies.

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_69ee883931888190901be96d75ee23cc completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f610f727ac819099df3683c15dc6cc completed May 2, 2026, 2:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a119a8d9c0c81908f9e1cc351167667 completed May 23, 2026, 12:16 p.m.
NEDg Description generation batch_6a119c7290c88190873129f6193a2121 completed May 23, 2026, 12:24 p.m.
NED2 Entity disambiguation (via description) batch_6a119cf886e88190aa83f0621903f248 completed May 23, 2026, 12:26 p.m.
Created at: April 26, 2026, 11:34 p.m.