Triple

T32471871
Position Surface form Disambiguated ID Type / Status
Subject Urmia University of Medical Sciences E829868 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Paramedical Sciences
The Faculty of Paramedical Sciences is an academic division specializing in training and research for allied health and paramedical professions within Urmia University of Medical Sciences.
E2007264 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 Paramedical Sciences | Statement: [Urmia University of Medical Sciences, hasFaculty, Faculty of Paramedical Sciences]
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 Paramedical Sciences
Triple: [Urmia University of Medical Sciences, hasFaculty, Faculty of Paramedical Sciences]
Generated description
The Faculty of Paramedical Sciences is an academic division specializing in training and research for allied health and paramedical professions within Urmia University of Medical 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_69f3491ee87c81908cbf5890079c2af6 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c35687ac81909e255d274c1fa164 completed May 3, 2026, 3:39 a.m.
NED1 Entity disambiguation (via context triple) batch_6a346699d02c8190ab26f2cd9fd9e5b5 completed June 18, 2026, 9:43 p.m.
NEDg Description generation batch_6a34678a344c8190a37da291fe37b6d1 completed June 18, 2026, 9:47 p.m.
NED2 Entity disambiguation (via description) batch_6a34682ebe448190b88c760af0af9dfa completed June 18, 2026, 9:50 p.m.
Created at: May 1, 2026, 12:57 a.m.