Triple

T36542512
Position Surface form Disambiguated ID Type / Status
Subject Tertiary Education Commission E901057 entity
Predicate coordinatesWith P1140 FINISHED
Object Education New Zealand
Education New Zealand is the New Zealand government agency responsible for promoting the country as an international education destination and supporting its international education sector.
E2188331 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: Education New Zealand | Statement: [Tertiary Education Commission, coordinatesWith, Education New Zealand]
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: Education New Zealand
Triple: [Tertiary Education Commission, coordinatesWith, Education New Zealand]
Generated description
Education New Zealand is the New Zealand government agency responsible for promoting the country as an international education destination and supporting its international education sector.

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_69f76e61217081908b79d610fe67b013 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7c243aa308190ad368856559bc36a completed May 3, 2026, 9:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39e6e33c5081908a84e46fe93a90d2 completed June 23, 2026, 1:52 a.m.
NEDg Description generation batch_6a39e81406b481909017a0c2c458fa71 completed June 23, 2026, 1:57 a.m.
NED2 Entity disambiguation (via description) batch_6a39e8762fe88190b0b12577b3d30410 completed June 23, 2026, 1:59 a.m.
Created at: May 3, 2026, 4:11 p.m.