Triple

T35745339
Position Surface form Disambiguated ID Type / Status
Subject Nehe City E1033160 entity
Predicate hasChineseName P4878 FINISHED
Object 讷河市
讷河市是中国黑龙江省西北部的一座县级市，隶属齐齐哈尔市，以农业为主并以寒地黑土资源和粮食产区而闻名。
 E2152939 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: 讷河市 | Statement: [Nehe City, hasChineseName, 讷河市]
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: 讷河市
Triple: [Nehe City, hasChineseName, 讷河市]
Generated description
讷河市是中国黑龙江省西北部的一座县级市，隶属齐齐哈尔市，以农业为主并以寒地黑土资源和粮食产区而闻名。

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_69f76e119d508190a3873cb302063832 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7a193bec481909a83b202d36d5e3d completed May 3, 2026, 7:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a387d28e7348190a209cbd52fc5403a completed June 22, 2026, 12:09 a.m.
NEDg Description generation batch_6a387db776108190be2cd974327d0a42 completed June 22, 2026, 12:11 a.m.
NED2 Entity disambiguation (via description) batch_6a387ea615a881909ab9f8e261a2d2f7 completed June 22, 2026, 12:15 a.m.
Created at: May 3, 2026, 4:06 p.m.