Triple

T26766998
Position Surface form Disambiguated ID Type / Status
Subject Murrindindi Shire E674965 entity
Predicate hasLocality P7943 FINISHED
Object Murrindindi
Murrindindi is a rural locality in central Victoria, Australia, known for its forested landscapes, agriculture, and proximity to natural attractions such as the Yea River and nearby state forests.
E1749481 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: Murrindindi | Statement: [Murrindindi Shire, hasLocality, Murrindindi]
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: Murrindindi
Triple: [Murrindindi Shire, hasLocality, Murrindindi]
Generated description
Murrindindi is a rural locality in central Victoria, Australia, known for its forested landscapes, agriculture, and proximity to natural attractions such as the Yea River and nearby state forests.

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_69eecda85298819097ee1c38a3d772e7 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f619289e008190a0f99326509ff9cb completed May 2, 2026, 3:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12298053f08190b536019bb3c29880 completed May 23, 2026, 10:26 p.m.
NEDg Description generation batch_6a122a2048408190a3a8cf5a2efa9b08 completed May 23, 2026, 10:28 p.m.
NED2 Entity disambiguation (via description) batch_6a122ac1713481909a80761bb471ef49 completed May 23, 2026, 10:31 p.m.
Created at: April 27, 2026, 4 a.m.