Triple

T12958976
Position Surface form Disambiguated ID Type / Status
Subject Tyler Hoechlin E310087 entity
Predicate givenName P17 FINISHED
Object Tyler
Tyler is a masculine given name commonly used in English-speaking countries, originally derived from an occupational surname meaning "tile maker" or "roof tiler."
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 Hoechlin, givenName, Tyler]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tyler
Context triple: [Tyler Hoechlin, givenName, Tyler]
  • A. John
    John is the given name of John M. Grunsfeld, an American physicist, former NASA astronaut, and leader in space science and exploration.
  • B. John
    John is the given name of John Eales, the renowned former Australian rugby union captain and World Cup winner.
  • C. John
    John II Casimir Vasa was a 17th-century King of Poland and Grand Duke of Lithuania from the Swedish House of Vasa.
  • D. John
    John McDowell is a prominent South African-born philosopher known for his influential work in epistemology, philosophy of mind, and ethics.
  • E. John
    John Cicero was a late 15th-century Elector of Brandenburg from the House of Hohenzollern who helped consolidate the territory’s political and administrative structures within the Holy Roman Empire.
  • 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 Hoechlin, givenName, Tyler]
Generated description
Tyler is a masculine given name commonly used in English-speaking countries, originally derived from an occupational surname meaning "tile maker" or "roof tiler."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Tyler
Target entity description: Tyler is a masculine given name commonly used in English-speaking countries, originally derived from an occupational surname meaning "tile maker" or "roof tiler."
  • 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 the officer in a Masonic lodge responsible for guarding the entrance and ensuring only qualified individuals are admitted to meetings.
  • D. 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.
  • 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_69d7bdfb57a88190836b743e2825feca completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d97e2e44908190bb8b43fc5c3b8a8a completed April 10, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6b8dddd80819094c80ef405024419 completed May 3, 2026, 2:54 a.m.
NEDg Description generation batch_69f6ba0904e8819098bae29961bf0046 completed May 3, 2026, 2:59 a.m.
NED2 Entity disambiguation (via description) batch_69f6bb8216fc8190a0119d434adf13a5 completed May 3, 2026, 3:05 a.m.
Created at: April 9, 2026, 5:44 p.m.