Triple

T15197544
Position Surface form Disambiguated ID Type / Status
Subject Karen Sillas E363175 entity
Predicate familyName P18 FINISHED
Object Sillas
Sillas is a surname most notably associated with American actress Karen Sillas, known for her work in independent film and television.
E1142850 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: Sillas | Statement: [Karen Sillas, familyName, Sillas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sillas
Context triple: [Karen Sillas, familyName, Sillas]
  • A. Silla
    Silla was an ancient Korean kingdom that unified most of the Korean Peninsula in the 7th century and played a central role in the development of early Korean culture, Buddhism, and statehood.
  • B. Asientos
    Asientos is a historic mining town and municipality in the northeastern part of the Mexican state of Aguascalientes.
  • C. Sofades
    Sofades is a town and municipality in central Greece, located in the Thessaly region within the Karditsa regional unit.
  • D. Sitton
    Sitton is a surname of English origin borne by various notable individuals, including athletes and public figures.
  • E. Mikeschair
    Mikeschair is a contemporary Christian music band known for their melodic worship songs and inspirational lyrics.
  • 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: Sillas
Triple: [Karen Sillas, familyName, Sillas]
Generated description
Sillas is a surname most notably associated with American actress Karen Sillas, known for her work in independent film and television.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Sillas
Target entity description: Sillas is a surname most notably associated with American actress Karen Sillas, known for her work in independent film and television.
  • A. Silla
    Silla was an ancient Korean kingdom that unified most of the Korean Peninsula in the 7th century and played a central role in the development of early Korean culture, Buddhism, and statehood.
  • B. Asientos
    Asientos is a historic mining town and municipality in the northeastern part of the Mexican state of Aguascalientes.
  • C. Sofades
    Sofades is a town and municipality in central Greece, located in the Thessaly region within the Karditsa regional unit.
  • D. Sitton
    Sitton is a surname of English origin borne by various notable individuals, including athletes and public figures.
  • E. Mikeschair
    Mikeschair is a contemporary Christian music band known for their melodic worship songs and inspirational lyrics.
  • 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0067fcc788190abdc083d4eadeb36 completed April 15, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fed3342624819087be35acadd88136 completed May 9, 2026, 6:24 a.m.
NEDg Description generation batch_69fed516a2008190bab6da27d28289e7 completed May 9, 2026, 6:32 a.m.
NED2 Entity disambiguation (via description) batch_69fed57659a081909ec777549deff505 completed May 9, 2026, 6:34 a.m.
Created at: April 10, 2026, 3:10 a.m.