Triple

T12320066
Position Surface form Disambiguated ID Type / Status
Subject Tyler Summitt E293703 entity
Predicate givenName P17 FINISHED
Object Tyler
Tyler is a masculine given name of English origin that is commonly used in the United States and other English-speaking countries.
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 Summitt, givenName, Tyler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tyler
Context triple: [Tyler Summitt, givenName, Tyler]
  • A. John
    John is the first name of Johnny Evers, a Hall of Fame American Major League Baseball second baseman who starred for the Chicago Cubs in the early 20th century.
  • B. John
    John is the middle name of Samuel John Mills, an American Congregationalist minister known for his role in early 19th-century missionary movements.
  • C. John
    John is the given name of John Eales, the renowned former Australian rugby union captain and World Cup winner.
  • D. 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.
  • E. John
    John McDowell is a prominent South African-born philosopher known for his influential work in epistemology, philosophy of mind, and ethics.
  • 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 Summitt, givenName, Tyler]
Generated description
Tyler is a masculine given name of English origin that is commonly used in the United States and other English-speaking countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tyler
Target entity description: Tyler is a masculine given name of English origin that is commonly used in the United States and other English-speaking countries.
  • 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 the officer in a Masonic lodge responsible for guarding the entrance and ensuring only qualified individuals are admitted to meetings.
  • E. Tyler
    Tyler is a fictional character appearing in the American television series "Kristin."
  • 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_69d6ab6ae0dc8190b1522a9c1c55c114 completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f4c2b548190938fff9427f07dc7 completed April 10, 2026, 6:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69f61e8aa94881908e4c184062037ab5 completed May 2, 2026, 3:55 p.m.
NEDg Description generation batch_69f61f5e20cc8190a84f50ddded76974 completed May 2, 2026, 3:59 p.m.
NED2 Entity disambiguation (via description) batch_69f62045b20c819083c755fbe99a9a7f completed May 2, 2026, 4:03 p.m.
Created at: April 8, 2026, 9:53 p.m.