Triple

T29942653
Position Surface form Disambiguated ID Type / Status
Subject Royal College, Colombo E760538 entity
Predicate alumniOrganization P14540 FINISHED
Object Royal College Union
The Royal College Union is the official alumni association of Royal College, Colombo, bringing together former students to support the school’s development and foster networking among its graduates.
E1892954 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: Royal College Union | Statement: [Royal College, Colombo, alumniOrganization, Royal College Union]
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: Royal College Union
Triple: [Royal College, Colombo, alumniOrganization, Royal College Union]
Generated description
The Royal College Union is the official alumni association of Royal College, Colombo, bringing together former students to support the school’s development and foster networking among its graduates.

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_69f22463f3648190a603c3ff305c660b completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f67808eb0c819087b96b4fcf4eaa18 completed May 2, 2026, 10:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27142c4ca48190a1d43bc96b9536e1 completed June 8, 2026, 7:12 p.m.
NEDg Description generation batch_6a2714fad8188190bf86af12ee777b53 completed June 8, 2026, 7:16 p.m.
NED2 Entity disambiguation (via description) batch_6a27198a097c8190aea66eba80acc1d8 completed June 8, 2026, 7:35 p.m.
Created at: April 29, 2026, 6:23 p.m.