Triple

T33836817
Position Surface form Disambiguated ID Type / Status
Subject Lama Temple E867254 entity
Predicate hasChineseName P4878 FINISHED
Object 雍和宫
雍和宫是位于北京市的一座著名藏传佛教寺院和皇家寺庙建筑群，以其宏伟的建筑和宗教文化价值闻名。
 E2069801 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: 雍和宫 | Statement: [Lama Temple, hasChineseName, 雍和宫]
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: 雍和宫
Triple: [Lama Temple, hasChineseName, 雍和宫]
Generated description
雍和宫是位于北京市的一座著名藏传佛教寺院和皇家寺庙建筑群，以其宏伟的建筑和宗教文化价值闻名。

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_69f34992ad40819087760ed939bd2a7a completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f7002f38308190af948b97a35a5b09 completed May 3, 2026, 7:58 a.m.
NED1 Entity disambiguation (via context triple) batch_6a366eab59248190bacb19fb2d390aec completed June 20, 2026, 10:42 a.m.
NEDg Description generation batch_6a366fbbedfc8190ad0d687723c177e1 completed June 20, 2026, 10:47 a.m.
NED2 Entity disambiguation (via description) batch_6a36710d8bf081909ea6d06eca8ebdda completed June 20, 2026, 10:53 a.m.
Created at: May 1, 2026, 1:47 a.m.