Triple

T14031755
Position Surface form Disambiguated ID Type / Status
Subject Kylian Mbappé E337606 entity
Predicate youthClub P1088 FINISHED
Object AS Bondy E613622 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: AS Bondy | Statement: [Kylian Mbappé, youthClub, AS Bondy]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: AS Bondy
Context triple: [Kylian Mbappé, youthClub, AS Bondy]
  • A. Bondy chosen
    Bondy is a suburban commune in the northeastern outskirts of Paris, France, served by regional rail and other public transport links into the capital.
  • B. Brian Bondy
    Brian Bondy is a software engineer and entrepreneur best known as a co-founder and former CTO of the privacy-focused Brave web browser.
  • C. Andy Bond
    Andy Bond is a British businessman best known for serving as the chief executive of Asda, one of the UK’s largest supermarket chains.
  • D. Eppstein
    Eppstein is a small historic town in the German state of Hesse, known for its medieval castle and scenic location in the Taunus mountains.
  • E. Sudie Bond
    Sudie Bond was an American character actress known for her work in film, television, and theater, often appearing in supporting comedic and dramatic roles.
  • 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2fab17008190981f1808726fa11c completed April 14, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbc337a5cc8190953b84255a401ada completed May 6, 2026, 10:39 p.m.
Created at: April 9, 2026, 10:20 p.m.