Triple

T2413005
Position Surface form Disambiguated ID Type / Status
Subject Snapchat E52237 entity
Predicate founder P104 FINISHED
Object Reggie Brown E56448 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: Reggie Brown | Statement: [Snapchat, founder, Reggie Brown]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reggie Brown
Context triple: [Snapchat, founder, Reggie Brown]
  • A. Reggie Brown chosen
    Reggie Brown is an American entrepreneur best known as a co-founder of Snapchat, the multimedia messaging app developed by Snap Inc.
  • B. Richard Briers
    Richard Briers was an English actor best known for his work in British television comedies such as "The Good Life" and for his frequent collaborations with director Kenneth Branagh in both stage and film.
  • C. Michael Hordern
    Michael Hordern was an English actor renowned for his distinguished stage and screen career, often noted for his Shakespearean roles and character work in British film and television.
  • D. Roger Lloyd-Pack
    Roger Lloyd-Pack was an English actor best known for his comedic roles in British television, particularly as Trigger in "Only Fools and Horses."
  • E. Terence Stamp
    Terence Stamp is an English actor known for his distinctive presence and performances in films such as "The Collector," "Superman II," and "The Limey."
  • 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_69ab495622948190bc6bc6e4cddaf645 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc92a4e1c8190819fa676295ac145 completed March 7, 2026, 6:43 a.m.
NED1 Entity disambiguation (via context triple) batch_69b055a71200819095c2a5481c61deb5 completed March 10, 2026, 5:32 p.m.
Created at: March 6, 2026, 9:41 p.m.