Triple

T2947976
Position Surface form Disambiguated ID Type / Status
Subject Ken Howery E79548 entity
Predicate givenName P17 FINISHED
Object Ken
Ken is a masculine given name commonly used in English-speaking countries, often as a short form of Kenneth.
 E126873 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: Ken | Statement: [Ken Howery, givenName, Ken]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ken
Context triple: [Ken Howery, givenName, Ken]
  • A. Ken
    Ken is the nickname of Ken Dryden, the legendary Canadian Hall of Fame goaltender best known for backstopping the Montreal Canadiens to multiple Stanley Cup championships in the 1970s.
  • B. Ken
    Ken is the iconic male doll character and Barbie’s counterpart, portrayed in the 2023 film as a comically self-aware and insecure figure exploring identity and patriarchy.
  • C. Kevin
    Kevin is the given name of Kevin Garnett, a Hall of Fame American professional basketball player known for his intensity, versatility, and NBA championship with the Boston Celtics.
  • D. Kar
    Kar is the young, streetwise pickpocket chosen as the reluctant successor to a mystical protector in the action film "Bulletproof Monk."
  • E. Ben
    Ben is a common given name, typically used as a short form of names like Benedict or Benjamin.
  • 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: Ken
Triple: [Ken Howery, givenName, Ken]
Generated description
Ken is a masculine given name commonly used in English-speaking countries, often as a short form of Kenneth.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ken
Target entity description: Ken is a masculine given name commonly used in English-speaking countries, often as a short form of Kenneth.
  • A. Ken
    Ken is the nickname of Ken Dryden, the legendary Canadian Hall of Fame goaltender best known for backstopping the Montreal Canadiens to multiple Stanley Cup championships in the 1970s.
  • B. Ken chosen
    Ken is the iconic male doll character and Barbie’s counterpart, portrayed in the 2023 film as a comically self-aware and insecure figure exploring identity and patriarchy.
  • C. Kevin
    Kevin is the given name of Kevin Garnett, a Hall of Fame American professional basketball player known for his intensity, versatility, and NBA championship with the Boston Celtics.
  • D. Kar
    Kar is the young, streetwise pickpocket chosen as the reluctant successor to a mystical protector in the action film "Bulletproof Monk."
  • E. Ben
    Ben is a common given name, typically used as a short form of names like Benedict or Benjamin.
  • 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_69ad8b1089588190b74d9e2505e45762 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad98b5916c8190b1163bf0b7fa136a completed March 8, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69b08695bea08190abce552493abda57 completed March 10, 2026, 9:01 p.m.
NEDg Description generation batch_69b0d4d08d688190888459d7d4fbd8d4 completed March 11, 2026, 2:34 a.m.
NED2 Entity disambiguation (via description) batch_69b0d5567e488190b5eee8a494433ae4 completed March 11, 2026, 2:37 a.m.
Created at: March 8, 2026, 2:57 p.m.