Triple

T24811604
Position Surface form Disambiguated ID Type / Status
Subject Jackée Harry E620800 entity
Predicate spouse P13 FINISHED
Object Elgin Charles Williams
Elgin Charles Williams is an American celebrity hairstylist and salon owner known for his work with high-profile clients and his former marriage to actress Jackée Harry.
E1652971 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: Elgin Charles Williams | Statement: [Jackée Harry, spouse, Elgin Charles Williams]
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: Elgin Charles Williams
Triple: [Jackée Harry, spouse, Elgin Charles Williams]
Generated description
Elgin Charles Williams is an American celebrity hairstylist and salon owner known for his work with high-profile clients and his former marriage to actress Jackée Harry.

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_69f4220b04448190bd04aee42d710fdc 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.