Triple
T15845905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adam Brody |
E384211
|
entity |
| Predicate | appearedIn |
P795
|
FINISHED |
| Object | StartUp |
E288547
|
NE FINISHED |
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 | Statement: [Adam Brody, appearedIn, StartUp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: StartUp Context triple: [Adam Brody, appearedIn, StartUp]
-
A.
StartUp (TV series)
chosen
StartUp is a crime drama television series that follows a group of unlikely partners who launch a controversial digital currency startup amid the dangerous world of organized crime and corrupt law enforcement.
-
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.
Startup Manager
Startup Manager is a macOS boot utility that lets users choose which available disk or volume to start their Mac from during startup.
-
D.
Startup.com
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.
-
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 (3 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e14ca636048190bc89cd8b4efa89e9 |
completed | April 16, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa143dcb48190951648edeae6542d |
completed | May 9, 2026, 9:04 p.m. |
Created at: April 10, 2026, 4:50 a.m.