Triple

T30630462
Position Surface form Disambiguated ID Type / Status
Subject University of Computer Studies, Yangon E779699 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Computer Science
The Faculty of Computer Science is an academic division specializing in computer science education and research within the University of Computer Studies, Yangon.
E1924195 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 Computer Science | Statement: [University of Computer Studies, Yangon, hasFaculty, Faculty of Computer Science]
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 Computer Science
Triple: [University of Computer Studies, Yangon, hasFaculty, Faculty of Computer Science]
Generated description
The Faculty of Computer Science is an academic division specializing in computer science education and research within the University of Computer Studies, Yangon.

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_69f224a431548190a44ad9d088dbf91f completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68a1d060c81908a5a9524876f04ed completed May 2, 2026, 11:34 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2863ecaa74819096746ba53f3444fa completed June 9, 2026, 7:05 p.m.
NEDg Description generation batch_6a28679a33cc81908a409c6da1f369da completed June 9, 2026, 7:20 p.m.
NED2 Entity disambiguation (via description) batch_6a2868229b108190ba4e742ebeb68179 completed June 9, 2026, 7:23 p.m.
Created at: April 29, 2026, 8:28 p.m.