Triple

T13011384
Position Surface form Disambiguated ID Type / Status
Subject looking_for_eric E322419 entity
Predicate platform P1292 FINISHED
Object Rotten Tomatoes E126670 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: Rotten Tomatoes | Statement: [looking_for_eric, platform, Rotten Tomatoes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rotten Tomatoes
Context triple: [looking_for_eric, platform, Rotten Tomatoes]
  • A. Rotten Tomatoes chosen
    Rotten Tomatoes is a popular online review aggregation platform that compiles film and television critics’ reviews into a percentage-based “Tomatometer” score.
  • B. Metacritic
    Metacritic is a review aggregation website that compiles and averages critics’ and users’ scores for films, games, TV shows, and music.
  • C. YTS
    YTS is the IATA airport code for Timmins Victor M. Power Airport, a regional airport serving the city of Timmins in Ontario, Canada.
  • D. Ebert
    Ebert is a German surname most notably associated with prominent political figures such as Friedrich Ebert, the first President of Germany, and his son Friedrich Ebert Jr.
  • E. At the Movies
    At the Movies was a long-running American film review television program, best known for featuring critics like Roger Ebert who popularized the "thumbs up/thumbs down" style of movie criticism.
  • 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_69d807657e8c8190bd9435ee2f823845 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e9e14b88190a2cee8e0c9bf31c8 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6c10d5b9881909db688c1ab0e6a77 completed May 3, 2026, 3:29 a.m.
Created at: April 9, 2026, 8:49 p.m.