Triple

T35027919
Position Surface form Disambiguated ID Type / Status
Subject Market Street, Sydney E1010392 entity
Predicate crosses P416 FINISHED
Object York Street, Sydney
York Street, Sydney is a major north–south thoroughfare in Sydney’s central business district, lined with commercial buildings and connecting key city landmarks and transport hubs.
 E2128566 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: York Street, Sydney | Statement: [Market Street, Sydney, crosses, York Street, Sydney]
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: York Street, Sydney
Triple: [Market Street, Sydney, crosses, York Street, Sydney]
Generated description
York Street, Sydney is a major north–south thoroughfare in Sydney’s central business district, lined with commercial buildings and connecting key city landmarks and transport hubs.

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_69f76dccf0108190af43b465d3750196 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f7854441e081908c6066125c3ba574 completed May 3, 2026, 5:26 p.m.
NED1 Entity disambiguation (via context triple) batch_6a37fb00cf208190a084940b253a9f25 completed June 21, 2026, 2:53 p.m.
NEDg Description generation batch_6a37fbd574a48190bea1f7942d54ec3a completed June 21, 2026, 2:57 p.m.
NED2 Entity disambiguation (via description) batch_6a37fc5a3260819088e6dfc450a676a5 completed June 21, 2026, 2:59 p.m.
Created at: May 3, 2026, 4:01 p.m.