Triple

T34730419
Position Surface form Disambiguated ID Type / Status
Subject Hendra, Queensland E1001192 entity
Predicate region P40 FINISHED
Object Inner north-east Brisbane
Inner north-east Brisbane is a predominantly residential and mixed-use urban area of Brisbane, Queensland, encompassing several inner suburbs close to the city centre and major transport corridors.
E2110452 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: Inner north-east Brisbane | Statement: [Hendra, Queensland, region, Inner north-east Brisbane]
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: Inner north-east Brisbane
Triple: [Hendra, Queensland, region, Inner north-east Brisbane]
Generated description
Inner north-east Brisbane is a predominantly residential and mixed-use urban area of Brisbane, Queensland, encompassing several inner suburbs close to the city centre and major transport corridors.

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_69f76daeb6e48190a4c9a6b0edc80f72 completed May 3, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f779abc3048190bc1f5e57c494d959 completed May 3, 2026, 4:36 p.m.
NED1 Entity disambiguation (via context triple) batch_6a375bedf74c8190aba79056e2d3f149 completed June 21, 2026, 3:35 a.m.
NEDg Description generation batch_6a375d09dfbc81909eddba9593dafbb2 completed June 21, 2026, 3:39 a.m.
NED2 Entity disambiguation (via description) batch_6a3760f4f2c88190998d890243e41710 completed June 21, 2026, 3:56 a.m.
Created at: May 3, 2026, 3:59 p.m.