Triple

T29490301
Position Surface form Disambiguated ID Type / Status
Subject Hanggin Banner E748057 entity
Predicate hasChineseName P4878 FINISHED
Object 杭锦旗
杭锦旗是中国内蒙古自治区鄂尔多斯市下辖的一个旗级行政单位,以草原、沙漠景观和能源资源而闻名。
E1868257 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: 杭锦旗 | Statement: [Hanggin Banner, hasChineseName, 杭锦旗]
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: 杭锦旗
Triple: [Hanggin Banner, hasChineseName, 杭锦旗]
Generated description
杭锦旗是中国内蒙古自治区鄂尔多斯市下辖的一个旗级行政单位,以草原、沙漠景观和能源资源而闻名。

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_69f0bd448c6881908aa6b475cefd5ddc completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66c09d82c8190951186b067c8466c completed May 2, 2026, 9:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a25f127a24c8190b171fc4375f642c5 completed June 7, 2026, 10:31 p.m.
NEDg Description generation batch_6a25f647773c8190b06ba76b03d21919 completed June 7, 2026, 10:52 p.m.
NED2 Entity disambiguation (via description) batch_6a25fab4e98081908778fc17fc2a2f60 completed June 7, 2026, 11:11 p.m.
Created at: April 28, 2026, 4:13 p.m.