Triple

T31782900
Position Surface form Disambiguated ID Type / Status
Subject Sichuan University E811250 entity
Predicate hasHospital P105 FINISHED
Object West China Hospital
West China Hospital is a major teaching and research hospital in Chengdu, China, renowned for its large scale, advanced medical services, and affiliation with Sichuan University.
 E1980887 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: West China Hospital | Statement: [Sichuan University, hasHospital, West China 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: West China Hospital
Triple: [Sichuan University, hasHospital, West China Hospital]
Generated description
West China Hospital is a major teaching and research hospital in Chengdu, China, renowned for its large scale, advanced medical services, and affiliation with Sichuan University.

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_69f348e544a48190ab6e700b05f6438c completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6abe67e20819093e9c1a8f6b39e37 completed May 3, 2026, 1:59 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2e659484988190af0144d7232136b3 completed June 14, 2026, 8:25 a.m.
NEDg Description generation batch_6a2e6621cf288190a758693a6daa0964 completed June 14, 2026, 8:28 a.m.
NED2 Entity disambiguation (via description) batch_6a2e6683793c819095064af6842e3a76 completed June 14, 2026, 8:29 a.m.
Created at: April 30, 2026, 11:36 p.m.