Triple

T32447392
Position Surface form Disambiguated ID Type / Status
Subject Toyohashi University of Technology E829184 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Engineering
The Faculty of Engineering at Toyohashi University of Technology is an academic division specializing in advanced engineering education and research across multiple technological disciplines.
 E2009542 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 Engineering | Statement: [Toyohashi University of Technology, hasFaculty, Faculty of Engineering]
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 Engineering
Triple: [Toyohashi University of Technology, hasFaculty, Faculty of Engineering]
Generated description
The Faculty of Engineering at Toyohashi University of Technology is an academic division specializing in advanced engineering education and research across multiple technological 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_69f3491d2e5c819092b1c9535beff8ec completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c2e767bc8190b49fd0c4a557b464 completed May 3, 2026, 3:37 a.m.
NED1 Entity disambiguation (via context triple) batch_6a347048cd8481909800ee3d929abf3c completed June 18, 2026, 10:25 p.m.
NEDg Description generation batch_6a3472047c04819081be8ff5b449e977 completed June 18, 2026, 10:32 p.m.
NED2 Entity disambiguation (via description) batch_6a34726680b881909112da4bcdbf3cb5 completed June 18, 2026, 10:34 p.m.
Created at: May 1, 2026, 12:56 a.m.