Triple

T28412062
Position Surface form Disambiguated ID Type / Status
Subject Frank Moorhouse E719692 entity
Predicate awardReceived P11 FINISHED
Object Australian Literature Society Gold Medal
The Australian Literature Society Gold Medal is a prestigious Australian literary award presented annually for an outstanding work of fiction.
E1837251 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: Australian Literature Society Gold Medal | Statement: [Frank Moorhouse, awardReceived, Australian Literature Society Gold Medal]
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: Australian Literature Society Gold Medal
Triple: [Frank Moorhouse, awardReceived, Australian Literature Society Gold Medal]
Generated description
The Australian Literature Society Gold Medal is a prestigious Australian literary award presented annually for an outstanding work of fiction.

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_69eff6f0f37c8190b37bc6fab08a9449 completed April 27, 2026, 11:53 p.m.
NER Named-entity recognition batch_69f64dbc9be4819082633afaeb8136dc completed May 2, 2026, 7:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a24bb7daf2081909b4f39bf8e469801 completed June 7, 2026, 12:29 a.m.
NEDg Description generation batch_6a24c6be99c88190bc453b178830534f completed June 7, 2026, 1:17 a.m.
NED2 Entity disambiguation (via description) batch_6a24ca97cbcc81909259460b11b2df9a completed June 7, 2026, 1:34 a.m.
Created at: April 28, 2026, 1:27 a.m.