Triple

T30872785

Position	Surface form	Disambiguated ID	Type / Status
Subject	Deep learning techniques for music recommendation (doctoral work)	`E786387`	entity
Predicate	hasAuthorAffiliationAtTimeOfThesis	`P16016`	FINISHED
Object	Department of Electronics and Information Systems, Ghent University The Department of Electronics and Information Systems at Ghent University is an academic department specializing in research and education in electronics, computer science, and information systems technologies.	`E1934915`	NE FINISHED

How this triple was built (3 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 Electronics and Information Systems, Ghent University | Statement: [Deep learning techniques for music recommendation (doctoral work), hasAuthorAffiliationAtTimeOfThesis, Department of Electronics and Information Systems, Ghent University]

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 Electronics and Information Systems, Ghent University
Triple: [Deep learning techniques for music recommendation (doctoral work), hasAuthorAffiliationAtTimeOfThesis, Department of Electronics and Information Systems, Ghent University]

Generated description

The Department of Electronics and Information Systems at Ghent University is an academic department specializing in research and education in electronics, computer science, and information systems technologies.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: hasAuthorAffiliationAtTimeOfThesis
Context triple: [Deep learning techniques for music recommendation (doctoral work), hasAuthorAffiliationAtTimeOfThesis, Department of Electronics and Information Systems, Ghent University]

A. hasAuthorAffiliationAtTimeOfPublication chosen
Indicates that an author was affiliated with a particular organization or institution at the time a work was published.
B. hasAcademicAffiliation
Indicates that an entity is formally associated with an academic institution, such as through employment, enrollment, or official collaboration.
C. hasAuthorDissertation
Indicates a relationship where a dissertation is linked to the person who authored it.
D. subjectAffiliation
Indicates that a subject is formally associated or connected with a particular organization, group, or institution.
E. creatorAffiliationAtIntroduction
Indicates the organizational or institutional affiliation associated with a creator at the time the creator was first introduced or presented.
F. None of above.

Provenance (6 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_69f224b9df2c819086f55f8bcf7f382e`	completed	April 29, 2026, 3:33 p.m.
NER	Named-entity recognition	`batch_69f7979a073881909a4fde2558e6b6f3`	completed	May 3, 2026, 6:44 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a28c7dc2a5c8190a29da5029335f586`	completed	June 10, 2026, 2:11 a.m.
NEDg	Description generation	`batch_6a28c9d3aa008190b4d42d25f3e299a2`	completed	June 10, 2026, 2:20 a.m.
NED2	Entity disambiguation (via description)	`batch_6a28ca4d933481909469dd7aaf9754fd`	completed	June 10, 2026, 2:22 a.m.
PD	Predicate disambiguation	`batch_69f7961550f88190b7bb8a9155458b54`	completed	May 3, 2026, 6:38 p.m.

Created at: April 29, 2026, 8:48 p.m.