Triple

T26383640
Position Surface form Disambiguated ID Type / Status
Subject Bangshan District E663211 entity
Predicate hasAreaCodeSystem P35165 FINISHED
Object Chinese telephone numbering plan
The Chinese telephone numbering plan is the national system that defines how phone numbers are structured, assigned, and dialed throughout mainland China.
 E1720783 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: Chinese telephone numbering plan | Statement: [Bangshan District, hasAreaCodeSystem, Chinese 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: Chinese telephone numbering plan
Triple: [Bangshan District, hasAreaCodeSystem, Chinese telephone numbering plan]
Generated description
The Chinese telephone numbering plan is the national system that defines how phone numbers are structured, assigned, and dialed throughout mainland China.

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_69ee88374adc81909868f3bab374a32f completed April 26, 2026, 9:48 p.m.
NER Named-entity recognition batch_69f610779e3481909bda4d2b1c5c4cb0 completed May 2, 2026, 2:55 p.m.
NED1 Entity disambiguation (via context triple) batch_6a119a7b0a788190a4a22685d2fa8388 completed May 23, 2026, 12:15 p.m.
NEDg Description generation batch_6a119b15cbb4819087ea26f6c87d8732 completed May 23, 2026, 12:18 p.m.
NED2 Entity disambiguation (via description) batch_6a119bad614481909156c765ce266350 completed May 23, 2026, 12:21 p.m.
Created at: April 26, 2026, 11:20 p.m.