Triple

T31641596
Position Surface form Disambiguated ID Type / Status
Subject 26th Parliament of Turkey E807471 entity
Predicate hasSpeaker P981 FINISHED
Object İsmail Kahraman
İsmail Kahraman is a Turkish politician and lawyer who served as Speaker of the Grand National Assembly and has been a prominent figure in conservative politics in Turkey.
E2284498 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: İsmail Kahraman | Statement: [26th Parliament of Turkey, hasSpeaker, İsmail Kahraman]
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: İsmail Kahraman
Triple: [26th Parliament of Turkey, hasSpeaker, İsmail Kahraman]
Generated description
İsmail Kahraman is a Turkish politician and lawyer who served as Speaker of the Grand National Assembly and has been a prominent figure in conservative politics in Turkey.

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_69f348d9ce58819093ea2da83cbeeec1 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a91b0a648190864338e252f7a4a1 completed May 3, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a438ed111dc81909cd23c6b428b43e3 completed June 30, 2026, 9:39 a.m.
NEDg Description generation batch_6a438fd8686081909e87658bc0183d9b completed June 30, 2026, 9:43 a.m.
NED2 Entity disambiguation (via description) batch_6a43904f1f28819084d4f5365362e412 completed June 30, 2026, 9:45 a.m.
Created at: April 30, 2026, 10:49 p.m.