Triple
T19879518
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wojcicki family |
E477730
|
entity |
| Predicate | hasNotableMember |
P304
|
FINISHED |
| Object | Anne 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: Anne Wojcicki | Statement: [Wojcicki family, hasNotableMember, Anne Wojcicki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anne Wojcicki Context triple: [Wojcicki family, hasNotableMember, Anne Wojcicki]
-
A.
Anne Wojcicki
chosen
Anne Wojcicki is an American entrepreneur and co-founder of the personal genomics and biotechnology company 23andMe.
-
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.
Janet Wojcicki
Janet Wojcicki is an American epidemiologist and academic researcher known for her work in public health and nutrition.
-
D.
Elaine Ostrander
Elaine Ostrander is a prominent American geneticist renowned for her pioneering work using domestic dogs to uncover the genetic basis of complex traits and diseases.
-
E.
Jill P. Mesirov
Jill P. Mesirov is an American mathematician and computational biologist known for her work in bioinformatics and leadership roles in data science within academia and industry.
- 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.