Triple

T34067885
Position Surface form Disambiguated ID Type / Status
Subject Eastern District, Hong Kong E873676 entity
Predicate hasEducationalInstitution P113 FINISHED
Object Chinese International School
Chinese International School is a prominent private, bilingual English–Mandarin international school in Hong Kong offering education from primary through secondary levels.
E2081248 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: Chinese International School | Statement: [Eastern District, Hong Kong, hasEducationalInstitution, Chinese International School]
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: Chinese International School
Triple: [Eastern District, Hong Kong, hasEducationalInstitution, Chinese International School]
Generated description
Chinese International School is a prominent private, bilingual English–Mandarin international school in Hong Kong offering education from primary through secondary levels.

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_69f349a4af208190afa14888f9c9fb9d completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f70ba61fac81909f614db6c36b1103 completed May 3, 2026, 8:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a36ae4ba4e881908c1db6ba15fb80c8 completed June 20, 2026, 3:14 p.m.
NEDg Description generation batch_6a36aef5e62c81909695813abde97094 completed June 20, 2026, 3:17 p.m.
NED2 Entity disambiguation (via description) batch_6a36afe4c5cc81909ea9c8b3d3903db5 completed June 20, 2026, 3:21 p.m.
Created at: May 1, 2026, 1:52 a.m.