Triple

T15763542
Position Surface form Disambiguated ID Type / Status
Subject Rebecca Gleeson E382158 entity
Predicate placeOfResidence P75 FINISHED
Object Melbourne E4488 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: Melbourne | Statement: [Rebecca Gleeson, placeOfResidence, Melbourne]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Melbourne
Context triple: [Rebecca Gleeson, placeOfResidence, Melbourne]
  • A. Melbourne chosen
    Melbourne is a major Australian city known for its vibrant arts scene, diverse culture, and status as a leading center for sports and education.
  • B. Melbourne
    Melbourne is a historic market town in Derbyshire, England, known for its Georgian architecture and the notable Melbourne Hall and gardens.
  • C. Melbourne
    Melbourne is a coastal city in east-central Florida known for its beaches, aerospace and technology industries, and role as a commercial and cultural hub of the Space Coast.
  • D. Melbourn
    Melbourn is a village and civil parish in South Cambridgeshire, England, known for its historic architecture and rural community character.
  • E. Sydney
    Sydney is a recurring character in Alison Bechdel’s long-running comic strip "Dykes to Watch Out For," known for her sharp intellect and complex personal relationships within its ensemble cast.
  • 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_69d86da09a10819082fe9797b23e4664 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e050b6c9fc8190a1bcf763c4b04b12 completed April 16, 2026, 3 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff87611d488190857c99e166ac9e8f completed May 9, 2026, 7:13 p.m.
Created at: April 10, 2026, 4:47 a.m.