Triple

T38336012
Position Surface form Disambiguated ID Type / Status
Subject Paderborn University E1037957 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Cultural Studies
The Faculty of Cultural Studies is an academic division of Paderborn University focused on disciplines that explore culture, language, history, and society.
 E2266779 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 Cultural Studies | Statement: [Paderborn University, hasFaculty, Faculty of Cultural Studies]
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 Cultural Studies
Triple: [Paderborn University, hasFaculty, Faculty of Cultural Studies]
Generated description
The Faculty of Cultural Studies is an academic division of Paderborn University focused on disciplines that explore culture, language, history, and society.

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_69f76e20d65c81909619ac0dd85c56f0 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69fcc6bb9c648190801227300f627ec1 completed May 7, 2026, 5:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41a7ecbe84819087dddac5370e5e2b completed June 28, 2026, 11:02 p.m.
NEDg Description generation batch_6a41abf6e6c88190903eeb75fd3dc905 completed June 28, 2026, 11:19 p.m.
NED2 Entity disambiguation (via description) batch_6a41ac5d1fa881908faedb41784d3d3d completed June 28, 2026, 11:21 p.m.
Created at: May 3, 2026, 4:30 p.m.