Triple

T5071576
Position Surface form Disambiguated ID Type / Status
Subject AFI Award for Best Actress in a Lead Role E114292 entity
Predicate location P40 FINISHED
Object Australia E876 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: Australia | Statement: [AFI Award for Best Actress in a Lead Role, location, Australia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Australia
Context triple: [AFI Award for Best Actress in a Lead Role, location, Australia]
  • A. Australia chosen
    Australia is a large island continent and sovereign country in the Southern Hemisphere, known for its unique wildlife, diverse landscapes, and major cities such as Sydney and Melbourne.
  • B. Aust
    Aust is a small village in South Gloucestershire, England, situated near the Severn Estuary and known historically for its ferry crossing and proximity to the Severn Bridge.
  • C. Tasmania
    Tasmania is an island state of Australia known for its rugged wilderness, unique wildlife, and relatively cool maritime climate.
  • D. AU
    AU is the commonly used abbreviation for the African Union, a continental organization that promotes political and economic cooperation among African states.
  • E. AU
    AU is the commonly used abbreviation for Anna University, a prominent public technical university based in Chennai, India.
  • 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_69bd443cf28c8190ad371d603563dbdd completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd74ce140881909a2874663244c0db completed March 20, 2026, 4:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69beb10778208190a5c6a9457c085491 completed March 21, 2026, 2:53 p.m.
Created at: March 20, 2026, 1:39 p.m.