Triple

T29680924
Position Surface form Disambiguated ID Type / Status
Subject China Conservatory of Music E750948 entity
Predicate hasDepartment P35 FINISHED
Object Department of Composition
The Department of Composition is an academic unit at the China Conservatory of Music dedicated to training composers and fostering the creation of original musical works.
E1880498 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 Composition | Statement: [China Conservatory of Music, hasDepartment, Department of Composition]
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 Composition
Triple: [China Conservatory of Music, hasDepartment, Department of Composition]
Generated description
The Department of Composition is an academic unit at the China Conservatory of Music dedicated to training composers and fostering the creation of original musical works.

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_69f0d624d7b08190ba237d226f78d0d9 completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f67261a8f081908e1eca98a9b1ae23 completed May 2, 2026, 9:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a267ec3a1a881909c8d24d15ebe17cc completed June 8, 2026, 8:35 a.m.
NEDg Description generation batch_6a2682fadaf48190a4d691901671b579 completed June 8, 2026, 8:53 a.m.
NED2 Entity disambiguation (via description) batch_6a268ec8f3908190a8801d62e978ac15 completed June 8, 2026, 9:43 a.m.
Created at: April 28, 2026, 7:10 p.m.