Triple

T32299426
Position Surface form Disambiguated ID Type / Status
Subject Van Yuzuncu Yil University E825194 entity
Predicate hasFaculty P141 FINISHED
Object Faculty of Letters
The Faculty of Letters is a humanities-focused academic division of Van Yuzuncu Yil University offering programs in language, literature, history, and related disciplines.
 E2001655 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 Letters | Statement: [Van Yuzuncu Yil University, hasFaculty, Faculty of Letters]
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 Letters
Triple: [Van Yuzuncu Yil University, hasFaculty, Faculty of Letters]
Generated description
The Faculty of Letters is a humanities-focused academic division of Van Yuzuncu Yil University offering programs in language, literature, history, and related 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_69f349115304819084ee91d345b6c8aa completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bd70b43c819086d1e5f1df5aec86 completed May 3, 2026, 3:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a30570a2f2881908f27137c9bc702c1 completed June 15, 2026, 7:48 p.m.
NEDg Description generation batch_6a305ad097f481908935fa484b3aba59 completed June 15, 2026, 8:04 p.m.
NED2 Entity disambiguation (via description) batch_6a305b7872348190aaa5fd5c7eb1a955 completed June 15, 2026, 8:07 p.m.
Created at: May 1, 2026, 12:45 a.m.