Triple
T4560037
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mom |
E120569
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Eddie Gorodetsky |
E525936
|
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: Eddie Gorodetsky | Statement: [Mom, executiveProducer, Eddie Gorodetsky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eddie Gorodetsky Context triple: [Mom, executiveProducer, Eddie Gorodetsky]
-
A.
Eddie Gorodetsky
chosen
Eddie Gorodetsky is an American television writer and producer known for his work on popular sitcoms such as "Two and a Half Men," "The Big Bang Theory," and "Mom."
-
B.
Leo Salkin
Leo Salkin was an American animator, writer, and storyboard artist known for his work on mid-20th-century animated films and shorts.
-
C.
Edward Zorinsky
Edward Zorinsky was a U.S. Senator from Nebraska and former mayor of Omaha known for his moderate Democratic politics and service in the late 20th century.
-
D.
Guy Rothblum
Guy Rothblum is a theoretical computer scientist known for his work in cryptography and complexity theory.
-
E.
Daniel Goldberg
Daniel Goldberg is a Canadian film producer best known for his long-running collaboration with Ivan Reitman on comedies such as "Meatballs," "Stripes," and "Old School."
- 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_69bd4636f1648190a701445c2fcd9c17 |
completed | March 20, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69bd582b871c8190be0b70c76d639000 |
completed | March 20, 2026, 2:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bfa91dde508190abd38b4cf132ad5e |
completed | March 22, 2026, 8:32 a.m. |
Created at: March 20, 2026, 1:09 p.m.