Triple
T11108944
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tomas Mikolov |
E262703
|
entity |
| Predicate | coAuthor |
P398
|
FINISHED |
| Object |
Kai Chen
Kai Chen is a researcher known for co-authoring influential work in natural language processing and word embeddings alongside Tomas Mikolov.
|
E906313
|
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: Kai Chen | Statement: [Tomas Mikolov, coAuthor, Kai Chen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kai Chen Context triple: [Tomas Mikolov, coAuthor, Kai Chen]
-
A.
Yao Chen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
B.
Jerry Chen
Jerry Chen is a prominent venture capitalist and general partner at Greylock Partners, known for investing in enterprise software and infrastructure startups.
-
C.
Ian Chen
Ian Chen is a Taiwanese-American child actor best known for his roles in the TV series "Fresh Off the Boat" and the superhero film "Shazam!".
-
D.
Fala Chen
Fala Chen is a Chinese-American actress known for her work in both Asian television dramas and Hollywood films, including roles in major franchises.
-
E.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
- 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: Kai Chen Triple: [Tomas Mikolov, coAuthor, Kai Chen]
Generated description
Kai Chen is a researcher known for co-authoring influential work in natural language processing and word embeddings alongside Tomas Mikolov.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kai Chen Target entity description: Kai Chen is a researcher known for co-authoring influential work in natural language processing and word embeddings alongside Tomas Mikolov.
-
A.
Yao Chen
Yao Chen is a prominent Chinese actress and philanthropist known for her influential social media presence and advocacy on social issues.
-
B.
Jerry Chen
Jerry Chen is a prominent venture capitalist and general partner at Greylock Partners, known for investing in enterprise software and infrastructure startups.
-
C.
Ian Chen
Ian Chen is a Taiwanese-American child actor best known for his roles in the TV series "Fresh Off the Boat" and the superhero film "Shazam!".
-
D.
Fala Chen
Fala Chen is a Chinese-American actress known for her work in both Asian television dramas and Hollywood films, including roles in major franchises.
-
E.
John Cheng
John Cheng is a film producer best known for his work on the dark comedy movie "Horrible Bosses."
- 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_69d6aa9b46cc8190b19f9f0cc45bf322 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d79a67d10c8190815d4c27d55270e8 |
completed | April 9, 2026, 12:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e42d72f8f48190a7414119a6be9d5e |
completed | April 19, 2026, 1:18 a.m. |
| NEDg | Description generation | batch_69e4374700b881908ebb185ae020487b |
completed | April 19, 2026, 2 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e4399385c08190852c3cbd730a1f11 |
completed | April 19, 2026, 2:10 a.m. |
Created at: April 8, 2026, 9:27 p.m.