Triple

T27440528
Position Surface form Disambiguated ID Type / Status
Subject Universidade de Vigo E690917 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Economics and Business
The Faculty of Economics and Business is an academic unit of the Universidade de Vigo dedicated to teaching and research in economics, business administration, and related social sciences.
E1772839 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 and Business | Statement: [Universidade de Vigo, hasFaculty, Faculty of Economics and Business]
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 and Business
Triple: [Universidade de Vigo, hasFaculty, Faculty of Economics and Business]
Generated description
The Faculty of Economics and Business is an academic unit of the Universidade de Vigo dedicated to teaching and research in economics, business administration, and related social sciences.

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_69ef5200fa0481908e28508d6e2c149e completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d8ce3f4819091ce2f5f909d5124 completed May 2, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12b226da4881908ad5142ef991c110 completed May 24, 2026, 8:09 a.m.
NEDg Description generation batch_6a12b365f1fc81909dd44f94d75924e2 completed May 24, 2026, 8:14 a.m.
NED2 Entity disambiguation (via description) batch_6a12b42bd380819087489bdeb2dbfab7 completed May 24, 2026, 8:17 a.m.
Created at: April 27, 2026, 12:44 p.m.