Triple
T19879572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Esther Wojcicki |
E477730
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Janet 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: Janet Wojcicki | Statement: [Esther Wojcicki, child, Janet Wojcicki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Janet Wojcicki Context triple: [Esther Wojcicki, child, Janet Wojcicki]
-
A.
Janet Wojcicki
chosen
Janet Wojcicki is an American epidemiologist and academic researcher known for her work in public health and nutrition.
-
B.
Esther Wojcicki
Esther Wojcicki is an American journalist, educator, and author renowned for her innovative teaching methods and influence in media and technology education.
-
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.
Kristen Grauman
Kristen Grauman is a prominent computer scientist known for her research in computer vision and machine learning, particularly in visual recognition and video understanding.
- 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658dd869c81908aed91ee767f5f3d |
completed | April 20, 2026, 4:48 p.m. |
Created at: April 10, 2026, 1:52 p.m.