Triple

T4156576
Position Surface form Disambiguated ID Type / Status
Subject Full Circle with Michael Palin E91427 entity
Predicate featuresCity P7690 FINISHED
Object Sydney E8462 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: Sydney | Statement: [Full Circle with Michael Palin, featuresCity, Sydney]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sydney
Context triple: [Full Circle with Michael Palin, featuresCity, Sydney]
  • A. Sydney chosen
    Sydney is Australia's largest and most populous city, renowned for its iconic harbour, Opera House, and Harbour Bridge.
  • B. Sydney
    Sydney is the spirited, fashionable young woman who serves as the central heroine of Louisa May Alcott’s novel "An Old-Fashioned Girl."
  • C. Melbourne
    Melbourne is a major Australian city known for its vibrant arts scene, diverse culture, and status as a leading center for sports and education.
  • D. Melbourne
    Melbourne is a historic market town in Derbyshire, England, known for its Georgian architecture and the notable Melbourne Hall and gardens.
  • E. Melbourn
    Melbourn is a village and civil parish in South Cambridgeshire, England, known for its historic architecture and rural community character.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69aed9626ebc8190a39de631788bea3e completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af028fc11c819093fb2f616b97a694 completed March 9, 2026, 5:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69b589e9ff288190a8dfb62d32a330b5 completed March 14, 2026, 4:16 p.m.
Created at: March 9, 2026, 3:44 p.m.