Triple
T2231591
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SHVO |
E48777
|
entity |
| Predicate | chiefExecutiveOfficer |
P2568
|
FINISHED |
| Object | Michael Shvo |
E347767
|
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 Shvo | Statement: [SHVO, chiefExecutiveOfficer, Michael Shvo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Shvo Context triple: [SHVO, chiefExecutiveOfficer, Michael Shvo]
-
A.
Michael Shvo
chosen
Michael Shvo is a high-profile real estate developer and art collector known for leading luxury property projects in major global cities.
-
B.
Michael Kagan
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.
-
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.
Jay Shofet
Jay Shofet is an Israeli environmental advocate and sustainability professional known for his work in conservation and public policy.
-
E.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
- 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_69a88aa51b388190949868ec9766e587 |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc06b7374819089fe643e12797bfd |
completed | March 7, 2026, 6:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b324b9ba9c8190bfba5d7539cffeb2 |
completed | March 12, 2026, 8:40 p.m. |
Created at: March 4, 2026, 7:47 p.m.