Triple
T23349748
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Initialized Capital |
E591971
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object | Garry Tan |
—
|
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: Garry Tan | Statement: [Initialized Capital, foundedBy, Garry Tan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garry Tan Context triple: [Initialized Capital, foundedBy, Garry Tan]
-
A.
Garry Tan
chosen
Garry Tan is an American entrepreneur, investor, and YouTuber best known as the president and CEO of Y Combinator and a prominent early-stage startup backer.
-
B.
Adrian Tan
Adrian Tan was a prominent Singaporean lawyer and author best known for his satirical novels and leadership in the legal community.
-
C.
Adam Tan
Adam Tan is a Chinese business executive best known as a top leader of the HNA Group conglomerate.
-
D.
Daniel Chong
Daniel Chong is an American animator, writer, and director best known for creating the Cartoon Network animated series "We Bare Bears."
-
E.
Gabriel Goh
Gabriel Goh is a machine learning researcher known for his work at OpenAI, including co-developing the CLIP model for connecting images and text.
- 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_69e25d20e3d08190bcede87673cafb25 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f19a132da08190b30de610d34c96cc |
completed | April 29, 2026, 5:41 a.m. |
Created at: April 17, 2026, 5:19 p.m.