Triple
T18600821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chris Hegedus |
E454614
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Startup.com |
—
|
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: Startup.com | Statement: [Chris Hegedus, notableWork, Startup.com]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Startup.com Context triple: [Chris Hegedus, notableWork, Startup.com]
-
A.
Startup.com
chosen
Startup.com is a 2001 documentary film that chronicles the rise and fall of the dot-com startup GovWorks during the late-1990s internet boom.
-
B.
Startupland
Startupland is a memoir and business book by Zendesk co-founder Mikkel Svane that chronicles the early struggles, growth, and lessons learned in building a global startup.
-
C.
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.
-
D.
Y Combinator
Y Combinator is a prominent Silicon Valley startup accelerator known for funding and mentoring early-stage technology companies such as Airbnb, Dropbox, and Stripe.
-
E.
Startup School
Startup School is Y Combinator’s free online program that provides education, mentorship, and resources to help early-stage founders build and grow their startups.
- 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_69d8d38ae7e081908a98df1251842402 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5475112608190acacc5ac7a08c4a0 |
completed | April 19, 2026, 9:21 p.m. |
Created at: April 10, 2026, 11:45 a.m.