Triple

T33907331
Position Surface form Disambiguated ID Type / Status
Subject Graz University of Technology E869218 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Architecture
The Faculty of Architecture at Graz University of Technology is an academic division specializing in architectural education, research, and design.
 E869211 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 Architecture | Statement: [Graz University of Technology, hasFaculty, Faculty of Architecture]
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 Architecture
Triple: [Graz University of Technology, hasFaculty, Faculty of Architecture]
Generated description
The Faculty of Architecture at Graz University of Technology is an academic division specializing in architectural education, research, and design.

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_69f3499869bc8190b6c33a81686af226 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f701878c888190bd9ffd52bfabf79a completed May 3, 2026, 8:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a368247db708190ab313476a8650ef5 completed June 20, 2026, 12:06 p.m.
NEDg Description generation batch_6a36831f01fc8190a5e2026f6039883d completed June 20, 2026, 12:10 p.m.
NED2 Entity disambiguation (via description) batch_6a36845f64a081909fefa73b4fb194b4 completed June 20, 2026, 12:15 p.m.
Created at: May 1, 2026, 1:48 a.m.