Triple

T29516293
Position Surface form Disambiguated ID Type / Status
Subject 乌兰察布市 E748804 entity
Predicate hasAdministrativeDivision P747 FINISHED
Object 察右后旗
察右后旗是内蒙古自治区中部、乌兰察布市下辖的一个以农业和畜牧业为主的旗级行政单位。
E1873703 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: 察右后旗 | Statement: [乌兰察布市, hasAdministrativeDivision, 察右后旗]
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: 察右后旗
Triple: [乌兰察布市, hasAdministrativeDivision, 察右后旗]
Generated description
察右后旗是内蒙古自治区中部、乌兰察布市下辖的一个以农业和畜牧业为主的旗级行政单位。

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_69f0bd461c208190bec20bbf24e02cc5 completed April 28, 2026, 1:59 p.m.
NER Named-entity recognition batch_69f66c636f908190baa9787a988958af completed May 2, 2026, 9:28 p.m.
NED1 Entity disambiguation (via context triple) batch_6a262d5599cc8190bb4c9449379269d2 completed June 8, 2026, 2:47 a.m.
NEDg Description generation batch_6a26386e713881909b1fbb41eb26a5a1 completed June 8, 2026, 3:35 a.m.
NED2 Entity disambiguation (via description) batch_6a2638c8b1888190b6ff37e1941c7ed0 completed June 8, 2026, 3:36 a.m.
Created at: April 28, 2026, 4:37 p.m.