Triple

T32031618
Position Surface form Disambiguated ID Type / Status
Subject Murray Library E817976 entity
Predicate partOf P40 FINISHED
Object Library Services, University of Sunderland
Library Services, University of Sunderland is the university’s central library department responsible for managing its libraries, collections, and academic information resources for students and staff.
E1988716 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: Library Services, University of Sunderland | Statement: [Murray Library, partOf, Library Services, University of Sunderland]
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: Library Services, University of Sunderland
Triple: [Murray Library, partOf, Library Services, University of Sunderland]
Generated description
Library Services, University of Sunderland is the university’s central library department responsible for managing its libraries, collections, and academic information resources for students and staff.

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_69f348fbc8148190b3c0f95d4772b153 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6b494f66c8190ada271cdb0a21195 completed May 3, 2026, 2:36 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ed4f20c588190be38f8153d0b6b8a completed June 14, 2026, 4:21 p.m.
NEDg Description generation batch_6a2ed5c07e34819098385a0d7a928fa4 completed June 14, 2026, 4:24 p.m.
NED2 Entity disambiguation (via description) batch_6a2ed7379d088190b7481d5c7eb61b9f completed June 14, 2026, 4:30 p.m.
Created at: May 1, 2026, 12:18 a.m.