Triple

T23941256
Position Surface form Disambiguated ID Type / Status
Subject Great Southern region of Western Australia E602787 entity
Predicate hasLocalGovernmentArea P8215 FINISHED
Object Shire of Denmark
The Shire of Denmark is a coastal local government area in Western Australia known for its forests, beaches, and tourism-focused rural communities.
E1608340 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: Shire of Denmark | Statement: [Great Southern region of Western Australia, hasLocalGovernmentArea, Shire of Denmark]
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: Shire of Denmark
Triple: [Great Southern region of Western Australia, hasLocalGovernmentArea, Shire of Denmark]
Generated description
The Shire of Denmark is a coastal local government area in Western Australia known for its forests, beaches, and tourism-focused rural communities.

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_69e2953cf6e081909b8e25a10a52dddc completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f1d02a1b308190a2d101774b455417 completed April 29, 2026, 9:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f764eb038819095207e3cbb948f07 completed May 21, 2026, 9:17 p.m.
NEDg Description generation batch_6a0f7721b65481908b58b4d68e50768c completed May 21, 2026, 9:20 p.m.
NED2 Entity disambiguation (via description) batch_6a0f7895a12c8190999f81b9b4cd7b9b completed May 21, 2026, 9:26 p.m.
Created at: April 17, 2026, 9:09 p.m.