Triple

T27408651
Position Surface form Disambiguated ID Type / Status
Subject Sophia University E692079 entity
Predicate academicStructure P50 FINISHED
Object Faculty of Economics
The Faculty of Economics at Sophia University is an academic division specializing in economic theory, policy, and related social sciences within the university’s broader educational and research framework.
E1774129 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 Economics | Statement: [Sophia University, academicStructure, Faculty of Economics]
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 Economics
Triple: [Sophia University, academicStructure, Faculty of Economics]
Generated description
The Faculty of Economics at Sophia University is an academic division specializing in economic theory, policy, and related social sciences within the university’s broader educational and research framework.

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_69ef5205fc808190ad3efc5525b8e6d6 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62cd83da48190854bb397bfe4b3ba completed May 2, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbc3eab48190a806fe756db15d23 completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bc5e92b08190a2a7f60630f6d0ff completed May 24, 2026, 8:52 a.m.
NED2 Entity disambiguation (via description) batch_6a12bcd0c164819098f637afcad01642 completed May 24, 2026, 8:54 a.m.
Created at: April 27, 2026, 12:31 p.m.