Triple
T1111921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Udacity |
E11013
|
entity |
| Predicate | foundedBy |
P104
|
FINISHED |
| Object |
Mike Sokolsky
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
|
E259846
|
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: Mike Sokolsky | Statement: [Udacity, foundedBy, Mike Sokolsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mike Sokolsky Context triple: [Udacity, foundedBy, Mike Sokolsky]
-
A.
Michael Gelman
Michael Gelman is a longtime American television producer best known for his work shaping and overseeing the daytime talk show "Live!" through its various host pairings.
-
B.
Michael Filerman
Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
-
C.
Victor Rasuk
Victor Rasuk is an American actor known for roles in films like "Lords of Dogtown" and "How to Make It in America," as well as supporting parts in major franchises.
-
D.
Martin Lev
Martin Lev was a child actor best known for his role in the 1976 musical gangster film "Bugsy Malone."
-
E.
Sam Zussman
Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
- 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: Mike Sokolsky Triple: [Udacity, foundedBy, Mike Sokolsky]
Generated description
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mike Sokolsky Target entity description: Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
-
A.
Michael Gelman
Michael Gelman is a longtime American television producer best known for his work shaping and overseeing the daytime talk show "Live!" through its various host pairings.
-
B.
Michael Filerman
Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
-
C.
Victor Rasuk
Victor Rasuk is an American actor known for roles in films like "Lords of Dogtown" and "How to Make It in America," as well as supporting parts in major franchises.
-
D.
Martin Lev
Martin Lev was a child actor best known for his role in the 1976 musical gangster film "Bugsy Malone."
-
E.
Sam Zussman
Sam Zussman is a sports and media executive who serves as a top business leader for the NBA’s Brooklyn Nets organization.
- 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_69a493252a648190ac48f8742474a5e8 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4bb7742788190b320aec99e76ca41 |
completed | March 1, 2026, 10:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aea815a69c8190b5caf24e351bb53a |
completed | March 9, 2026, 10:59 a.m. |
| NEDg | Description generation | batch_69aea93ddce88190a268bed11c5a7167 |
completed | March 9, 2026, 11:04 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aea9b8dff08190a09f0c965dfd6738 |
completed | March 9, 2026, 11:06 a.m. |
Created at: March 1, 2026, 7:43 p.m.