Triple

T11898583
Position Surface form Disambiguated ID Type / Status
Subject Tyler Gregory Okonma E283094 entity
Predicate givenName P17 FINISHED
Object Tyler
Tyler is a common English given name, notably borne by American rapper, producer, and designer Tyler, the Creator (Tyler Gregory Okonma).
E674057 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 Gregory Okonma, givenName, Tyler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tyler
Context triple: [Tyler Gregory Okonma, givenName, Tyler]
  • A. John
    John is the birth name of American character actor Jack Warden, known for his prolific film and television career in the mid-20th century.
  • B. John
    John is the given name of John M. Grunsfeld, an American physicist, former NASA astronaut, and leader in space science and exploration.
  • C. John
    John is the given name of John Eales, the renowned former Australian rugby union captain and World Cup winner.
  • D. John
    John II Casimir Vasa was a 17th-century King of Poland and Grand Duke of Lithuania from the Swedish House of Vasa.
  • E. John
    John is the common given name of American author and YouTube creator John Green, known for novels like "The Fault in Our Stars" and for co-founding the Vlogbrothers channel.
  • 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 Gregory Okonma, givenName, Tyler]
Generated description
Tyler is a common English given name, notably borne by American rapper, producer, and designer Tyler, the Creator (Tyler Gregory Okonma).
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tyler
Target entity description: Tyler is a common English given name, notably borne by American rapper, producer, and designer Tyler, the Creator (Tyler Gregory Okonma).
  • A. Tyler chosen
    Tyler is a masculine given name commonly used in English-speaking countries, originally derived from an occupational surname meaning "tile maker" or "house builder."
  • B. Tyler
    Tyler is a surname most prominently associated with American actress Liv Tyler and various other notable figures in entertainment and public life.
  • C. Tyler
    Tyler is a mid-sized city in East Texas known for its rose cultivation, annual Texas Rose Festival, and role as a regional medical and educational hub.
  • D. Tyler
    Tyler is a fictional character appearing in the American television series "Kristin."
  • E. 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.
  • F. None of above.

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_69d6ab2a90b08190a4e818821cc93e6d completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8dd13cc10819089d8d5103e562924 completed April 10, 2026, 11:20 a.m.
NED1 Entity disambiguation (via context triple) batch_69f418205b788190a4b1b81d89cf7fff completed May 1, 2026, 3:04 a.m.
NEDg Description generation batch_69f41f1c21388190b6ecb0fd602abb7d completed May 1, 2026, 3:33 a.m.
NED2 Entity disambiguation (via description) batch_69f42283c4cc81909793834ef65d2514 completed May 1, 2026, 3:48 a.m.
Created at: April 8, 2026, 9:44 p.m.