Triple
T23045957
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jessica Livingston |
E573877
|
entity |
| Predicate | employer |
P7
|
FINISHED |
| Object | Y Combinator |
—
|
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: Y Combinator | Statement: [Jessica Livingston, employer, Y Combinator]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Y Combinator Context triple: [Jessica Livingston, employer, Y Combinator]
-
A.
Y Combinator
chosen
Y Combinator is a prominent Silicon Valley startup accelerator known for funding and mentoring early-stage technology companies such as Airbnb, Dropbox, and Stripe.
-
B.
500 Startups
500 Startups is a global venture capital firm and startup accelerator known for investing in and mentoring early-stage technology companies around the world.
-
C.
Thiel Capital
Thiel Capital is an investment firm and family office managing the personal capital and strategic ventures of billionaire entrepreneur and investor Peter Thiel.
-
D.
Union Square Ventures
Union Square Ventures is a New York–based venture capital firm known for early-stage investments in prominent internet and technology companies.
-
E.
KPCB
KPCB is a prominent Silicon Valley venture capital firm known for backing influential technology and life sciences companies from their early stages.
- 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_69e245b9c11481909d06c872214d21af |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f185192754819093a87d23371e7bbc |
completed | April 29, 2026, 4:12 a.m. |
Created at: April 17, 2026, 3:54 p.m.