Triple

T20140718
Position Surface form Disambiguated ID Type / Status
Subject Tyler (Carmen Sandiego) E491154 entity
Predicate hasTitle P38 FINISHED
Object Tyler
Tyler is a character from the Carmen Sandiego franchise, known primarily from the associated media and adaptations of the series.
E1412974 NE FINISHED

How this triple was built (4 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: Tyler | Statement: [Tyler (Carmen Sandiego), hasTitle, Tyler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tyler
Context triple: [Tyler (Carmen Sandiego), hasTitle, Tyler]
  • A. John
    John is the given name of John Eales, the renowned former Australian rugby union captain and World Cup winner.
  • B. John
    John is the first name of Jack Graney, a Canadian Major League Baseball player and later a pioneering baseball broadcaster.
  • C. John
    John is the given first name of the American Old West outlaw and gunfighter Johnny Ringo.
  • D. John
    John is the first name of American actor and musician John Stamos, best known for his role as Uncle Jesse on the television series "Full House."
  • E. John
    John is the given name of American novelist and historical fiction writer John Jakes, best known for his sprawling family sagas set during pivotal periods of U.S. history.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Tyler
Triple: [Tyler (Carmen Sandiego), hasTitle, Tyler]
Generated description
Tyler is a character from the Carmen Sandiego franchise, known primarily from the associated media and adaptations of the series.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tyler
Target entity description: Tyler is a character from the Carmen Sandiego franchise, known primarily from the associated media and adaptations of the series.
  • A. Tyler
    Tyler is the main character of the film "Return to Sender," around whom the story’s central events and conflicts revolve.
  • B. Tyler
    Tyler is a fictional character appearing in the American television series "Kristin."
  • C. Tyler
    Tyler is a surname most prominently associated with American actress Liv Tyler and various other notable figures in entertainment and public life.
  • D. Tyler
    Tyler is a character in the 2015 horror-thriller film "The Visit," serving as one of the two grandchildren whose unsettling stay with their grandparents drives the movie’s plot.
  • E. Tyler
    Tyler is the officer in a Masonic lodge responsible for guarding the entrance and ensuring only qualified individuals are admitted to meetings.
  • F. None of above. chosen

Provenance (5 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_69da6265f8f0819080b29c752a574088 completed April 11, 2026, 3:01 p.m.
NER Named-entity recognition batch_69e6679b179c8190a9511df8ed82098a completed April 20, 2026, 5:51 p.m.
NED1 Entity disambiguation (via context triple) batch_6a082dd9c1448190aa4038899470daf0 completed May 16, 2026, 8:42 a.m.
NEDg Description generation batch_6a082e93a1e88190bac5daae12ece9f2 completed May 16, 2026, 8:45 a.m.
NED2 Entity disambiguation (via description) batch_6a082f334dd481908edb6ea3ae07e0a7 completed May 16, 2026, 8:47 a.m.
Created at: April 11, 2026, 11:32 p.m.