Triple

T16955217
Position Surface form Disambiguated ID Type / Status
Subject Mount Kembla E411281 entity
Predicate nearbyLocality P4647 FINISHED
Object Figtree E74727 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: Figtree | Statement: [Mount Kembla, nearbyLocality, Figtree]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Figtree
Context triple: [Mount Kembla, nearbyLocality, Figtree]
  • A. Figtree chosen
    Figtree is a residential suburb of Wollongong in New South Wales, Australia, known for its shopping centre and proximity to both the Illawarra escarpment and the city centre.
  • B. Banyon
    Banyon is an American television detective series from the early 1970s centered on a hard-boiled private investigator in 1930s Los Angeles.
  • C. Nagi tree
    The Nagi tree is a revered sacred tree species in Japan, often associated with Shinto shrines and believed to offer protection and good fortune.
  • D. Viola Tree
    Viola Tree was a British actress and singer of the early 20th century, known for her stage performances and musical talents.
  • E. Liane
    The Liane is a coastal river in northern France that flows through the port city of Boulogne-sur-Mer into the English Channel.
  • 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_69d886c9c9d481909afe222093641cae completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d01bb700819082a441c124be3cb6 completed April 18, 2026, 6:40 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d464762c8190a734ffdd83633f70 completed May 10, 2026, 6:54 p.m.
Created at: April 10, 2026, 5:31 a.m.