Triple
T22227684
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | How to Raise Successful People |
E549387
|
entity |
| Predicate | author |
P4
|
FINISHED |
| Object | Esther Wojcicki |
—
|
NE NERFINISHED |
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: Esther Wojcicki | Statement: [How to Raise Successful People, author, Esther Wojcicki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Esther Wojcicki Context triple: [How to Raise Successful People, author, Esther Wojcicki]
-
A.
Esther Wojcicki
chosen
Esther Wojcicki is an American journalist, educator, and author renowned for her innovative teaching methods and influence in media and technology education.
-
B.
Janet Wojcicki
Janet Wojcicki is an American epidemiologist and academic researcher known for her work in public health and nutrition.
-
C.
Anne Wojcicki
Anne Wojcicki is an American entrepreneur and co-founder of the personal genomics and biotechnology company 23andMe.
-
D.
Stanley Wojcicki
Stanley Wojcicki is a Polish-American physicist and longtime Stanford University professor known both for his contributions to particle physics and as the father of tech executive Susan Wojcicki.
-
E.
Susan Wojcicki
Susan Wojcicki is an American technology executive best known for serving as the longtime CEO of YouTube and for being one of Google’s earliest employees and key advertising leaders.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e403d6481909a94d0aaf157f6ef |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12befe38c8190b547586e41b099b1 |
completed | April 28, 2026, 9:51 p.m. |
Created at: April 16, 2026, 8:37 p.m.