Triple

T25085485
Position Surface form Disambiguated ID Type / Status
Subject TU Dortmund University E628305 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Mathematics
The Faculty of Mathematics at TU Dortmund University is an academic division dedicated to research and teaching in pure and applied mathematics within the university’s science and engineering profile.
E617390 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 Mathematics | Statement: [TU Dortmund University, hasFaculty, Faculty of Mathematics]
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 Mathematics
Triple: [TU Dortmund University, hasFaculty, Faculty of Mathematics]
Generated description
The Faculty of Mathematics at TU Dortmund University is an academic division dedicated to research and teaching in pure and applied mathematics within the university’s science and engineering profile.

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_69e2ff2e73f881909992bf3eda5c25cb completed April 18, 2026, 3:49 a.m.
NER Named-entity recognition batch_69f461e3bef081908ef1c4d28cfe03e1 completed May 1, 2026, 8:18 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1048e5d6188190ba50f5bac4237e3b completed May 22, 2026, 12:15 p.m.
NEDg Description generation batch_6a104cbac0b48190ac739e35c090180f completed May 22, 2026, 12:31 p.m.
NED2 Entity disambiguation (via description) batch_6a104d2be4f08190a7a94bc1b04223cf completed May 22, 2026, 12:33 p.m.
Created at: April 18, 2026, 6:23 a.m.