Triple

T26369313
Position Surface form Disambiguated ID Type / Status
Subject Gulou District, Nanjing E660726 entity
Predicate hasNotableHospital P50261 FINISHED
Object Jiangsu Provincial People’s Hospital
Jiangsu Provincial People’s Hospital is a major comprehensive teaching and research hospital in Nanjing, serving as one of Jiangsu Province’s leading medical centers.
E1723916 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: Jiangsu Provincial People’s Hospital | Statement: [Gulou District, Nanjing, hasNotableHospital, Jiangsu Provincial People’s Hospital]
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: Jiangsu Provincial People’s Hospital
Triple: [Gulou District, Nanjing, hasNotableHospital, Jiangsu Provincial People’s Hospital]
Generated description
Jiangsu Provincial People’s Hospital is a major comprehensive teaching and research hospital in Nanjing, serving as one of Jiangsu Province’s leading medical centers.

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_69ee812a698881908d6a58265995fa39 completed April 26, 2026, 9:18 p.m.
NER Named-entity recognition batch_69fd59b4b6fc81909d6d773da55a31d3 completed May 8, 2026, 3:34 a.m.
NED1 Entity disambiguation (via context triple) batch_6a11aeb0f05881909a17f5879f5254f5 completed May 23, 2026, 1:42 p.m.
NEDg Description generation batch_6a11af4e7c608190a71debb7fc9c4b83 completed May 23, 2026, 1:44 p.m.
NED2 Entity disambiguation (via description) batch_6a11b071a8c48190a3b486d471e3e1a1 completed May 23, 2026, 1:49 p.m.
Created at: April 26, 2026, 10:57 p.m.