Triple

T27433263
Position Surface form Disambiguated ID Type / Status
Subject Sakarya University E690705 entity
Predicate hasAcademicUnit P1488 FINISHED
Object Faculty of Political Sciences
The Faculty of Political Sciences at Sakarya University is an academic unit specializing in the study and research of politics, public administration, and related social sciences.
E1772823 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 Political Sciences | Statement: [Sakarya University, hasAcademicUnit, Faculty of Political 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 Political Sciences
Triple: [Sakarya University, hasAcademicUnit, Faculty of Political Sciences]
Generated description
The Faculty of Political Sciences at Sakarya University is an academic unit specializing in the study and research of politics, public 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_69ef52003fb48190b0f1295246182a86 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d5bcfd08190a92bf6213a07769e completed May 2, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12b250a1308190a143ca7a952063f2 completed May 24, 2026, 8:09 a.m.
NEDg Description generation batch_6a12b34db36c81908d6f05b3e610013a 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, 12:43 p.m.