Triple

T33668672
Position Surface form Disambiguated ID Type / Status
Subject Kent Ridge MRT station E862558 entity
Predicate nearbyInstitution P6776 FINISHED
Object NUS School of Design and Environment
NUS School of Design and Environment is a faculty of the National University of Singapore specializing in architecture, industrial design, real estate, and related built-environment disciplines.
E2061560 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: NUS School of Design and Environment | Statement: [Kent Ridge MRT station, nearbyInstitution, NUS School of Design and Environment]
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: NUS School of Design and Environment
Triple: [Kent Ridge MRT station, nearbyInstitution, NUS School of Design and Environment]
Generated description
NUS School of Design and Environment is a faculty of the National University of Singapore specializing in architecture, industrial design, real estate, and related built-environment disciplines.

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_69f34984c4008190bb82f33a7819da64 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6fa3a06c48190b69d72ab4e82e852 completed May 3, 2026, 7:33 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36272e840c8190b2fc0c0ba5f8ea3b completed June 20, 2026, 5:37 a.m.
NEDg Description generation batch_6a36283aac9c8190836bddb4a59bb063 completed June 20, 2026, 5:42 a.m.
NED2 Entity disambiguation (via description) batch_6a3628b9d16481908e159baeeedd8c0d completed June 20, 2026, 5:44 a.m.
Created at: May 1, 2026, 1:42 a.m.