Triple
T23349743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Initialized Capital |
E591971
|
entity |
| Predicate | coFoundedBy |
P3263
|
FINISHED |
| Object |
Garry Tan
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.
|
E1582089
|
NE FINISHED |
How this triple was built (4 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, coFoundedBy, Garry Tan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garry Tan Context triple: [Initialized Capital, coFoundedBy, Garry Tan]
-
A.
Adrian Tan
Adrian Tan was a prominent Singaporean lawyer and author best known for his satirical novels and leadership in the legal community.
-
B.
Adam Tan
Adam Tan is a Chinese business executive best known as a top leader of the HNA Group conglomerate.
-
C.
Daniel Chong
Daniel Chong is an American animator, writer, and director best known for creating the Cartoon Network animated series "We Bare Bears."
-
D.
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.
-
E.
Ken Seng
Ken Seng is a cinematographer known for his visually distinctive work on films such as "Obsessed."
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Garry Tan Triple: [Initialized Capital, coFoundedBy, Garry Tan]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Garry Tan Target entity description: 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.
-
A.
Adrian Tan
Adrian Tan was a prominent Singaporean lawyer and author best known for his satirical novels and leadership in the legal community.
-
B.
Adam Tan
Adam Tan is a Chinese business executive best known as a top leader of the HNA Group conglomerate.
-
C.
Daniel Chong
Daniel Chong is an American animator, writer, and director best known for creating the Cartoon Network animated series "We Bare Bears."
-
D.
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.
-
E.
Ken Seng
Ken Seng is a cinematographer known for his visually distinctive work on films such as "Obsessed."
- F. None of above. chosen
Provenance (5 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. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c4cbbbf388190a2ec51bb06690168 |
completed | May 19, 2026, 11:42 a.m. |
| NEDg | Description generation | batch_6a0c59a18c5481909662cd338b797340 |
completed | May 19, 2026, 12:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c5a57cfb88190bb4e7f65807639b6 |
completed | May 19, 2026, 12:40 p.m. |
Created at: April 17, 2026, 5:19 p.m.