Triple

T15554846
Position Surface form Disambiguated ID Type / Status
Subject Lego Dimensions E370840 entity
Predicate developer P73 FINISHED
Object TT Games E504599 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: TT Games | Statement: [Lego Dimensions, developer, TT Games]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TT Games
Context triple: [Lego Dimensions, developer, TT Games]
  • A. TT Games chosen
    TT Games is a British video game developer best known for creating the popular series of LEGO-themed action-adventure games.
  • B. Torch Games
    Torch Games is a built-in casual gaming platform within the Torch web browser that lets users play games directly from the browser interface.
  • C. Titan Entertainment Group
    Titan Entertainment Group is a British media company best known for publishing comics, graphic novels, and related pop culture content through its Titan Comics imprint.
  • D. Vivendi Games
    Vivendi Games was a French video game publisher and holding company that owned studios like Blizzard Entertainment before merging with Activision to form Activision Blizzard.
  • E. Midway Games
    Midway Games was an American video game company best known for publishing and developing popular arcade and console titles such as the Mortal Kombat series.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04a96c0c88190808f68601a36b506 completed April 16, 2026, 2:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff456209288190aba6debd434af741 completed May 9, 2026, 2:32 p.m.
Created at: April 10, 2026, 4:09 a.m.