Triple

T28011223
Position Surface form Disambiguated ID Type / Status
Subject UCL Centre for Digital Humanities E707422 entity
Predicate affiliation P10 FINISHED
Object Department of Information Studies, UCL
The Department of Information Studies at UCL is a leading academic department specializing in library and information science, archives and records management, publishing, and digital humanities within University College London.
E1799302 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 Information Studies, UCL | Statement: [UCL Centre for Digital Humanities, affiliation, Department of Information Studies, UCL]
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 Information Studies, UCL
Triple: [UCL Centre for Digital Humanities, affiliation, Department of Information Studies, UCL]
Generated description
The Department of Information Studies at UCL is a leading academic department specializing in library and information science, archives and records management, publishing, and digital humanities within University College London.

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_69ef96ba350c81908230d0b501b974c4 completed April 27, 2026, 5:02 p.m.
NER Named-entity recognition batch_69f63bd9f5108190a8266bea76ef9513 completed May 2, 2026, 6 p.m.
NED1 Entity disambiguation (via context triple) batch_6a15b89f792c81908726ef846c61f2c5 completed May 26, 2026, 3:13 p.m.
NEDg Description generation batch_6a15ba7bfa288190ae476695e52a4398 completed May 26, 2026, 3:21 p.m.
NED2 Entity disambiguation (via description) batch_6a15bbab614c81908b86ec6c4043e1d0 completed May 26, 2026, 3:26 p.m.
Created at: April 27, 2026, 8:03 p.m.