Triple

T27371923
Position Surface form Disambiguated ID Type / Status
Subject Kadir Has University E690346 entity
Predicate hasLibrary P35 FINISHED
Object Kadir Has University Library
Kadir Has University Library is the main academic library of Kadir Has University in Istanbul, providing students and researchers with access to scholarly resources, study spaces, and information services.
 E1769772 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: Kadir Has University Library | Statement: [Kadir Has University, hasLibrary, Kadir Has University Library]
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: Kadir Has University Library
Triple: [Kadir Has University, hasLibrary, Kadir Has University Library]
Generated description
Kadir Has University Library is the main academic library of Kadir Has University in Istanbul, providing students and researchers with access to scholarly resources, study spaces, and information services.

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_69ef51ff826081909e42c8e2bfb97941 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62c62e6c88190924d41dabdaabd1e completed May 2, 2026, 4:54 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7e41c9c8190a8500f89e550fb21 completed May 24, 2026, 7:25 a.m.
NEDg Description generation batch_6a12a9604bd88190870f35189d3080ea completed May 24, 2026, 7:31 a.m.
NED2 Entity disambiguation (via description) batch_6a12aa051060819082b52092cdccd0d5 completed May 24, 2026, 7:34 a.m.
Created at: April 27, 2026, 12:19 p.m.