Triple

T5599470
Position Surface form Disambiguated ID Type / Status
Subject George Senesky E147079 entity
Predicate familyName P18 FINISHED
Object Senesky
Senesky is a surname most notably associated with George Senesky, an American professional basketball player and coach in the mid-20th century.
 E528892 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: Senesky | Statement: [George Senesky, familyName, Senesky]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Senesky
Context triple: [George Senesky, familyName, Senesky]
  • A. Sene
    Sene is the tenth month of the Ethiopian calendar, roughly corresponding to June in the Gregorian calendar.
  • B. Es Sénia
    Es Sénia is a commune and suburb of Oran in northwestern Algeria, known for hosting the region’s main international airport and various industrial and educational facilities.
  • C. Senne
    The Senne is a small river flowing through Brussels, Belgium, much of which has been covered over as the city developed.
  • D. Mistinguett
    Mistinguett was a famous French actress and singer of the early 20th century, celebrated as one of Paris’s most iconic music-hall stars.
  • E. Sinegal
    Sinegal is a surname most notably associated with James Sinegal, the co-founder and longtime CEO of Costco Wholesale Corporation.
  • 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: Senesky
Triple: [George Senesky, familyName, Senesky]
Generated description
Senesky is a surname most notably associated with George Senesky, an American professional basketball player and coach in the mid-20th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Senesky
Target entity description: Senesky is a surname most notably associated with George Senesky, an American professional basketball player and coach in the mid-20th century.
  • A. Sene
    Sene is the tenth month of the Ethiopian calendar, roughly corresponding to June in the Gregorian calendar.
  • B. Es Sénia
    Es Sénia is a commune and suburb of Oran in northwestern Algeria, known for hosting the region’s main international airport and various industrial and educational facilities.
  • C. Senne
    The Senne is a small river flowing through Brussels, Belgium, much of which has been covered over as the city developed.
  • D. Mistinguett
    Mistinguett was a famous French actress and singer of the early 20th century, celebrated as one of Paris’s most iconic music-hall stars.
  • E. Sinegal
    Sinegal is a surname most notably associated with James Sinegal, the co-founder and longtime CEO of Costco Wholesale Corporation.
  • 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_69c009043d648190a7af89698ccf1e3e completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c020d936dc8190a2e599f1df9fdd91 completed March 22, 2026, 5:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0287139508190aa646918228cfdc0 completed March 22, 2026, 5:35 p.m.
NEDg Description generation batch_69c0350eb53081909dc573fefa3e7f0a completed March 22, 2026, 6:29 p.m.
NED2 Entity disambiguation (via description) batch_69c036ee4e1c8190b9e60655d72407ff completed March 22, 2026, 6:37 p.m.
Created at: March 22, 2026, 3:38 p.m.