Triple

T25440441
Position Surface form Disambiguated ID Type / Status
Subject Department of Water Resources Engineering, Chittagong University of Engineering and Technology E637488 entity
Predicate hasParentOrganization P10 FINISHED
Object Faculty of Civil Engineering, Chittagong University of Engineering and Technology
The Faculty of Civil Engineering at Chittagong University of Engineering and Technology is an academic division that oversees civil engineering education and research, including specialized departments such as Water Resources Engineering.
 E1683220 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 Civil Engineering, Chittagong University of Engineering and Technology | Statement: [Department of Water Resources Engineering, Chittagong University of Engineering and Technology, hasParentOrganization, Faculty of Civil Engineering, Chittagong University of Engineering and Technology]
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 Civil Engineering, Chittagong University of Engineering and Technology
Triple: [Department of Water Resources Engineering, Chittagong University of Engineering and Technology, hasParentOrganization, Faculty of Civil Engineering, Chittagong University of Engineering and Technology]
Generated description
The Faculty of Civil Engineering at Chittagong University of Engineering and Technology is an academic division that oversees civil engineering education and research, including specialized departments such as Water Resources Engineering.

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_69e75db6c97081908178383fa632b193 completed April 21, 2026, 11:21 a.m.
NER Named-entity recognition batch_69f5f6e5451c8190a6a6f6a938167985 completed May 2, 2026, 1:06 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10ad5b326c8190aed92e0ab29f5aff completed May 22, 2026, 7:24 p.m.
NEDg Description generation batch_6a10aeae38748190a970045e9bbd49f7 completed May 22, 2026, 7:29 p.m.
NED2 Entity disambiguation (via description) batch_6a10af719e6c8190bbd23598b3426106 completed May 22, 2026, 7:33 p.m.
Created at: April 21, 2026, 2 p.m.