Triple
T7424030
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | If/Then |
E171322
|
entity |
| Predicate | orchestrator |
P4735
|
FINISHED |
| Object | Michael Starobin |
E310997
|
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 Starobin | Statement: [If/Then, orchestrator, Michael Starobin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Michael Starobin Context triple: [If/Then, orchestrator, Michael Starobin]
-
A.
Michael Starobin
chosen
Michael Starobin is a Tony Award–winning American orchestrator and arranger known for his work on numerous Broadway musicals.
-
B.
Michael Rachmil
Michael Rachmil is a film producer best known for his work on the 1987 romantic comedy "Roxanne" starring Steve Martin.
-
C.
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.
-
D.
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.
-
E.
Mike Sokolsky
Mike Sokolsky is a co-founder of the online education platform Udacity, known for its technology-focused courses and nanodegree programs.
- 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_69c68a625d048190af70eb8b63bec5a0 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f2eece588190905774e7151edcb8 |
completed | March 27, 2026, 9:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c937a70a94819090ff91dfa463682b |
completed | March 29, 2026, 2:31 p.m. |
Created at: March 27, 2026, 3:12 p.m.