Triple

T24811430
Position Surface form Disambiguated ID Type / Status
Subject Anjelah Johnson E620796 entity
Predicate notableCharacter P1481 FINISHED
Object Tammy the nail salon worker
Tammy the nail salon worker is a popular comedic character portrayed by Anjelah Johnson in her stand-up routines, known for her exaggerated accent and humorous depictions of nail salon culture.
E1652956 NE FINISHED

How this triple was built (2 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: Tammy the nail salon worker | Statement: [Anjelah Johnson, notableCharacter, Tammy the nail salon worker]
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: Tammy the nail salon worker
Triple: [Anjelah Johnson, notableCharacter, Tammy the nail salon worker]
Generated description
Tammy the nail salon worker is a popular comedic character portrayed by Anjelah Johnson in her stand-up routines, known for her exaggerated accent and humorous depictions of nail salon culture.

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_69e2fabf26bc8190b191faac8f67065b completed April 18, 2026, 3:30 a.m.
NER Named-entity recognition batch_69f42209c0ec819084b93b661292046b completed May 1, 2026, 3:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a101c3a2144819095440b806c0ac045 completed May 22, 2026, 9:04 a.m.
NEDg Description generation batch_6a1024aba7c88190a2d72c92bf9e5723 completed May 22, 2026, 9:40 a.m.
NED2 Entity disambiguation (via description) batch_6a102561fda081908fc03becd4d997ac completed May 22, 2026, 9:44 a.m.
Created at: April 18, 2026, 4:50 a.m.