Triple

T8461519

Position	Surface form	Disambiguated ID	Type / Status
Subject	Daye City	`E200049`	entity
Predicate	hasNameInChinese	`P4878`	FINISHED
Object	大冶市大冶市 is a county-level city in southeastern Hubei Province, China, known historically for its rich mineral resources and metal smelting industry.	`E734769`	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: 大冶市 | Statement: [Daye City, hasNameInChinese, 大冶市]

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

Target entity: 大冶市
Context triple: [Daye City, hasNameInChinese, 大冶市]

A. 黄冈市
黄冈市是中国湖北省东部的一座地级市，位于长江中游、与武汉相邻，以其红色革命历史和丰富的自然资源而闻名。
B. 新余市
新余市 is a county-level city in central Jiangxi Province, China, known for its steel industry and rapid industrial development.
C. 汉阳
汉阳是中国湖北省武汉市的一个历史悠久的城区，位于长江与汉江交汇处，以其工业基础和文化遗产而闻名。
D. 马鞍山
马鞍山是位于中国安徽省东部、长江沿岸的一座以钢铁工业和山水景观著称的地级市。
E. 张湾区
张湾区是湖北省十堰市下辖的一个市辖区和主要城区之一，以工业基础和城市综合功能较为发达而著称。
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: 大冶市
Triple: [Daye City, hasNameInChinese, 大冶市]

Generated description

大冶市 is a county-level city in southeastern Hubei Province, China, known historically for its rich mineral resources and metal smelting industry.

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

Target entity: 大冶市
Target entity description: 大冶市 is a county-level city in southeastern Hubei Province, China, known historically for its rich mineral resources and metal smelting industry.

A. 黄冈市
黄冈市是中国湖北省东部的一座地级市，位于长江中游、与武汉相邻，以其红色革命历史和丰富的自然资源而闻名。
B. 新余市
新余市 is a county-level city in central Jiangxi Province, China, known for its steel industry and rapid industrial development.
C. 汉阳
汉阳是中国湖北省武汉市的一个历史悠久的城区，位于长江与汉江交汇处，以其工业基础和文化遗产而闻名。
D. 马鞍山
马鞍山是位于中国安徽省东部、长江沿岸的一座以钢铁工业和山水景观著称的地级市。
E. 张湾区
张湾区是湖北省十堰市下辖的一个市辖区和主要城区之一，以工业基础和城市综合功能较为发达而著称。
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_69ca83198c4c8190a337bf717d1813f5`	completed	March 30, 2026, 2:05 p.m.
NER	Named-entity recognition	`batch_69cbe4a0ff4c8190b17bbd33db632cea`	completed	March 31, 2026, 3:13 p.m.
NED1	Entity disambiguation (via context triple)	`batch_69ce1df5d76881909ea5040824985cc3`	completed	April 2, 2026, 7:42 a.m.
NEDg	Description generation	`batch_69ce1f88d404819096c6024c0e61d1ea`	completed	April 2, 2026, 7:49 a.m.
NED2	Entity disambiguation (via description)	`batch_69ce209463508190bdbed7271241807f`	completed	April 2, 2026, 7:53 a.m.

Created at: March 30, 2026, 6:10 p.m.