Triple

T2216385
Position Surface form Disambiguated ID Type / Status
Subject Marv Films E48040 entity
Predicate workOn P30363 FINISHED
Object Tetris E244892 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: Tetris | Statement: [Marv Films, workOn, Tetris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tetris
Context triple: [Marv Films, workOn, Tetris]
  • A. Tetris chosen
    Tetris is a classic and highly influential puzzle video game in which players rotate and arrange falling geometric shapes to complete and clear horizontal lines.
  • B. Tetris Hall Kiev
    Tetris Hall Kiev is a distinctive residential complex in Kyiv, Ukraine, known for its stacked, Tetris-like architectural form and contemporary design.
  • C. Pacman
    Pacman is a lightweight, command-line package manager originally developed for Arch Linux, known for its speed and simple binary package handling.
  • D. Minesweeper
    Minesweeper is a classic single-player logic puzzle video game in which players uncover squares on a grid while avoiding hidden mines.
  • E. Taitō
    Taitō is a special ward in central Tokyo known for its historic districts, traditional temples, and major cultural attractions such as Ueno Park and Asakusa.
  • 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_69a88aa1ee708190862c8c378c41e9eb completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc00f4c3881909d03301fcdfa8b67 completed March 7, 2026, 6:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae6afa4f4481908cd1559624805559 completed March 9, 2026, 6:38 a.m.
Created at: March 4, 2026, 7:46 p.m.