Triple

T25709035
Position Surface form Disambiguated ID Type / Status
Subject Saray E644677 entity
Predicate hasAreaCodeSystem P35165 FINISHED
Object Turkish telephone numbering plan
The Turkish telephone numbering plan is the national system that defines how phone numbers are structured, assigned, and dialed across Turkey, including area codes for cities and regions.
 E1691183 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: Turkish telephone numbering plan | Statement: [Saray, hasAreaCodeSystem, Turkish telephone numbering plan]
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: Turkish telephone numbering plan
Triple: [Saray, hasAreaCodeSystem, Turkish telephone numbering plan]
Generated description
The Turkish telephone numbering plan is the national system that defines how phone numbers are structured, assigned, and dialed across Turkey, including area codes for cities and regions.

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_69e77e83c8ec8190bf52fcdac4838984 completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fc1385c4819082eff6432380dd2c completed May 2, 2026, 1:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10c172e81c8190b6980a8fda133b9e completed May 22, 2026, 8:49 p.m.
NEDg Description generation batch_6a10c22b19a48190b04130bdb7763f0a completed May 22, 2026, 8:52 p.m.
NED2 Entity disambiguation (via description) batch_6a10c2de07648190858ba8901748aa53 completed May 22, 2026, 8:55 p.m.
Created at: April 21, 2026, 9:08 p.m.