Triple

T24675273
Position Surface form Disambiguated ID Type / Status
Subject 1988 Rugby League World Cup E610966 entity
Predicate coachOfWinningTeam P10030 FINISHED
Object Don Furner
Don Furner was an Australian rugby league coach and administrator best known for his successful tenure with the Canberra Raiders and his role in guiding top-level representative sides.
E1655262 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: Don Furner | Statement: [1988 Rugby League World Cup, coachOfWinningTeam, Don Furner]
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: Don Furner
Triple: [1988 Rugby League World Cup, coachOfWinningTeam, Don Furner]
Generated description
Don Furner was an Australian rugby league coach and administrator best known for his successful tenure with the Canberra Raiders and his role in guiding top-level representative sides.

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_69e2c4d5c2dc8190ac857dea25ec6ce9 completed April 17, 2026, 11:40 p.m.
NER Named-entity recognition batch_69f40faf3b54819095936eea14333029 completed May 1, 2026, 2:27 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1032f0e5788190a69a73175c6ec1cd completed May 22, 2026, 10:41 a.m.
NEDg Description generation batch_6a1033999eb8819093313456a2a6fb1b completed May 22, 2026, 10:44 a.m.
NED2 Entity disambiguation (via description) batch_6a10344ac26c81908a031f43caf710b5 completed May 22, 2026, 10:47 a.m.
Created at: April 18, 2026, 3:03 a.m.