Triple

T19927293
Position Surface form Disambiguated ID Type / Status
Subject Varketili E478957 entity
Predicate locatedInNeighborhood P40 FINISHED
Object Varketili NE NERFINISHED

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: Varketili | Statement: [Varketili, locatedInNeighborhood, Varketili]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Varketili
Context triple: [Varketili, locatedInNeighborhood, Varketili]
  • A. Varketili chosen
    Varketili is a metro station in Tbilisi, Georgia, serving as a key terminus on the Tbilisi Metro network.
  • B. Veltro
    Veltro is the nickname of the Macchi C.205, an Italian World War II fighter aircraft renowned for its speed and agility.
  • C. Kerevi
    Kerevi is a surname most prominently associated with Samu Kerevi, a professional rugby union player who has represented Australia internationally.
  • D. Erista
    Erista is the internal codename for Nvidia’s original Tegra X1 system-on-chip, notably used in the first-generation Nintendo Switch.
  • E. Davigo
    Davigo is an Italian surname most notably associated with Piercamillo Davigo, a prominent magistrate and former member of Italy’s anti-corruption "Mani Pulite" (Clean Hands) investigation.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e521855c8190b41871700afc8d6a completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e659ca52c881908dc8053bf61be4c4 completed April 20, 2026, 4:52 p.m.
Created at: April 10, 2026, 1:53 p.m.