Triple

T38693346
Position Surface form Disambiguated ID Type / Status
Subject George Street, Sydney E949928 entity
Predicate hasNeighbourhood P4813 FINISHED
Object Town Hall area
Town Hall area is a central commercial and transport precinct in Sydney’s CBD, centered around Sydney Town Hall and its busy surrounding streets.
 E2281275 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: Town Hall area | Statement: [George Street, Sydney, hasNeighbourhood, Town Hall area]
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: Town Hall area
Triple: [George Street, Sydney, hasNeighbourhood, Town Hall area]
Generated description
Town Hall area is a central commercial and transport precinct in Sydney’s CBD, centered around Sydney Town Hall and its busy surrounding streets.

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_69f76f0124408190bb39c3040734846b completed May 3, 2026, 3:51 p.m.
NER Named-entity recognition batch_69fcdc6640948190b98aec6cc03b2cb1 completed May 7, 2026, 6:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4205c84630819089a8645f96e36e40 completed June 29, 2026, 5:42 a.m.
NEDg Description generation batch_6a4207b79f7c8190ac996d0300bedb29 completed June 29, 2026, 5:50 a.m.
NED2 Entity disambiguation (via description) batch_6a42087b26e48190a29e08c752c67fb7 completed June 29, 2026, 5:54 a.m.
Created at: May 3, 2026, 4:33 p.m.