Triple

T30061518
Position Surface form Disambiguated ID Type / Status
Subject Beaconhouse National University E763900 entity
Predicate hasFaculty P141 FINISHED
Object School of Architecture
The School of Architecture is an academic unit of Beaconhouse National University that offers professional architectural education and related design programs.
 E1899767 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: School of Architecture | Statement: [Beaconhouse National University, hasFaculty, School of Architecture]
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: School of Architecture
Triple: [Beaconhouse National University, hasFaculty, School of Architecture]
Generated description
The School of Architecture is an academic unit of Beaconhouse National University that offers professional architectural education and related design programs.

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_69f224716378819087a722e487832b70 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67ca408f88190991b5a628e2834c3 completed May 2, 2026, 10:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27431462188190a62b4b2f2a5d18e7 completed June 8, 2026, 10:32 p.m.
NEDg Description generation batch_6a2743bb3a8081908e963d8e8f4a9abc completed June 8, 2026, 10:35 p.m.
NED2 Entity disambiguation (via description) batch_6a2744c1a1d881908e1e9a00a065a253 completed June 8, 2026, 10:40 p.m.
Created at: April 29, 2026, 6:58 p.m.