Triple

T21717667
Position Surface form Disambiguated ID Type / Status
Subject Hotel E536072 entity
Predicate starred P5563 FINISHED
Object Nathan Cook
Nathan Cook is a hospitality professional recognized for his notable role in the hotel industry.
E1499716 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: Nathan Cook | Statement: [Hotel, starred, Nathan Cook]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nathan Cook
Context triple: [Hotel, starred, Nathan Cook]
  • A. Jacob Cook
    Jacob Cook was an early settler and prominent local figure after whom the community of Cooksville was named.
  • B. Douglas Cook
    Douglas Cook was an American screenwriter best known for co-writing action and thriller films such as "The Rock" and "Double Jeopardy."
  • C. Phil Cookson
    Phil Cookson is a relatively obscure individual whose primary public mention appears to be as a namesake in reference data, with no widely documented achievements or roles.
  • D. Jon Cooksey
    Jon Cooksey is a television and film writer best known for co-writing the popular Disney Channel movie "Halloweentown."
  • E. Edward Tyas Cook
    Edward Tyas Cook was a British journalist, editor, and biographer best known for his influential work in late 19th- and early 20th-century liberal newspapers and for editing the writings of John Ruskin.
  • 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: Nathan Cook
Triple: [Hotel, starred, Nathan Cook]
Generated description
Nathan Cook is a hospitality professional recognized for his notable role in the hotel industry.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Nathan Cook
Target entity description: Nathan Cook is a hospitality professional recognized for his notable role in the hotel industry.
  • A. Jacob Cook
    Jacob Cook was an early settler and prominent local figure after whom the community of Cooksville was named.
  • B. Douglas Cook
    Douglas Cook was an American screenwriter best known for co-writing action and thriller films such as "The Rock" and "Double Jeopardy."
  • C. Phil Cookson
    Phil Cookson is a relatively obscure individual whose primary public mention appears to be as a namesake in reference data, with no widely documented achievements or roles.
  • D. Jon Cooksey
    Jon Cooksey is a television and film writer best known for co-writing the popular Disney Channel movie "Halloweentown."
  • E. Edward Tyas Cook
    Edward Tyas Cook was a British journalist, editor, and biographer best known for his influential work in late 19th- and early 20th-century liberal newspapers and for editing the writings of John Ruskin.
  • 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_69e0c46c6dd88190a595375fa6ebd701 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69efd96babdc81908226ec043dbe7431 completed April 27, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a2ec80c508190b46dcd4db623f900 completed May 17, 2026, 9:10 p.m.
NEDg Description generation batch_6a0a30ab32f08190b49005a37ce281b6 completed May 17, 2026, 9:18 p.m.
NED2 Entity disambiguation (via description) batch_6a0a319460888190b74341c50719ac80 completed May 17, 2026, 9:22 p.m.
Created at: April 16, 2026, 6:47 p.m.