Triple

T3846993

Position	Surface form	Disambiguated ID	Type / Status
Subject	Circular Mound Altar	`E85193`	entity
Predicate	ChineseName	`P744`	FINISHED
Object	圜丘坛圜丘坛是位于北京天坛内、明清两代皇帝举行祭天大典的重要露天祭坛建筑。	`E394046`	NE FINISHED

How this triple was built (4 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: 圜丘坛 | Statement: [Circular Mound Altar, ChineseName, 圜丘坛]

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: 圜丘坛
Context triple: [Circular Mound Altar, ChineseName, 圜丘坛]

A. 太庙
太庙是北京故宫东侧一座明清皇家宗庙建筑群，现为对公众开放的历史文化景区。
B. 天安门
天安门是位于北京市中心、作为中国象征性地标和重要政治历史事件发生地的著名城门与广场名称。
C. 颐和园
颐和园是位于北京市西北部、以宏伟的皇家园林建筑和昆明湖、万寿山自然景观著称的世界文化遗产。
D. 午门
午门 is the grand southern entrance and main gate of Beijing’s Forbidden City, historically used for important imperial ceremonies and proclamations.
E. 中和殿
中和殿 is a ceremonial hall within Beijing’s Forbidden City, historically used by Chinese emperors to rest and prepare before major state rituals.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

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: 圜丘坛
Triple: [Circular Mound Altar, ChineseName, 圜丘坛]

Generated description

圜丘坛是位于北京天坛内、明清两代皇帝举行祭天大典的重要露天祭坛建筑。

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: 圜丘坛
Target entity description: 圜丘坛是位于北京天坛内、明清两代皇帝举行祭天大典的重要露天祭坛建筑。

A. 太庙
太庙是北京故宫东侧一座明清皇家宗庙建筑群，现为对公众开放的历史文化景区。
B. 天安门
天安门是位于北京市中心、作为中国象征性地标和重要政治历史事件发生地的著名城门与广场名称。
C. 颐和园
颐和园是位于北京市西北部、以宏伟的皇家园林建筑和昆明湖、万寿山自然景观著称的世界文化遗产。
D. 午门
午门 is the grand southern entrance and main gate of Beijing’s Forbidden City, historically used for important imperial ceremonies and proclamations.
E. 中和殿
中和殿 is a ceremonial hall within Beijing’s Forbidden City, historically used by Chinese emperors to rest and prepare before major state rituals.
F. None of above. chosen

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_69aed936de1c81908f91bed80f70abb2`	completed	March 9, 2026, 2:29 p.m.
NER	Named-entity recognition	`batch_69aeebcb069881909d3536b18b7802a7`	completed	March 9, 2026, 3:48 p.m.
NED1	Entity disambiguation (via context triple)	`batch_69b50414acdc81909bf0b62afa3fe536`	completed	March 14, 2026, 6:45 a.m.
NEDg	Description generation	`batch_69b50585106c8190aaa1c47b397543ea`	completed	March 14, 2026, 6:51 a.m.
NED2	Entity disambiguation (via description)	`batch_69b50707d6a4819097f2bca0ebe663b1`	completed	March 14, 2026, 6:58 a.m.

Created at: March 9, 2026, 3:18 p.m.