Triple

T37007968
Position Surface form Disambiguated ID Type / Status
Subject S1 Yingbin Expressway E915855 entity
Predicate roadSystem P3293 FINISHED
Object Shanghai S-route system
The Shanghai S-route system is a network of numbered expressways and ring roads that forms the core high-speed road infrastructure within the Shanghai metropolitan area.
 E2209099 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: Shanghai S-route system | Statement: [S1 Yingbin Expressway, roadSystem, Shanghai S-route system]
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: Shanghai S-route system
Triple: [S1 Yingbin Expressway, roadSystem, Shanghai S-route system]
Generated description
The Shanghai S-route system is a network of numbered expressways and ring roads that forms the core high-speed road infrastructure within the Shanghai metropolitan area.

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_69f76e90ed548190b187d2475f5c807d completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fa00377d208190bf90dc02590a543f completed May 5, 2026, 2:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e576cbc948190994ec2e9037ff01c completed June 26, 2026, 10:41 a.m.
NEDg Description generation batch_6a3e58dc8de4819094600ec666db679a completed June 26, 2026, 10:47 a.m.
NED2 Entity disambiguation (via description) batch_6a3e660e8b2481909452759b4815549a completed June 26, 2026, 11:44 a.m.
Created at: May 3, 2026, 4:14 p.m.