Triple
T1893521
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mellanox Technologies |
E41924
|
entity |
| Predicate | keyPerson |
P256
|
FINISHED |
| Object | Michael Kagan |
E306985
|
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: Michael Kagan | Statement: [Mellanox Technologies, keyPerson, Michael Kagan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Kagan Context triple: [Mellanox Technologies, keyPerson, Michael Kagan]
-
A.
Michael Kagan
chosen
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
-
B.
Don Katz
Don Katz is an American entrepreneur and author best known as the founder of the audiobook and spoken-word entertainment company Audible.
-
C.
Mike Sokolsky
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
-
D.
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.
-
E.
Andrew Rabinovich
Andrew Rabinovich is a computer scientist and researcher known for his contributions to computer vision and deep learning, including influential work at Google.
- 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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1497df08190ad90dd89f76208ca |
completed | March 7, 2026, 5:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b055827da881909a589a8d5c577eb9 |
completed | March 10, 2026, 5:31 p.m. |
Created at: March 4, 2026, 7:34 p.m.