Triple
T16105390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lenny |
E390725
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Marvin Worth |
E294798
|
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: Marvin Worth | Statement: [Lenny, producer, Marvin Worth]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marvin Worth Context triple: [Lenny, producer, Marvin Worth]
-
A.
Marvin Worth
chosen
Marvin Worth was an American film and television producer and screenwriter best known for biographical projects such as the Muhammad Ali film "The Greatest" and the Lenny Bruce biopic "Lenny."
-
B.
Henry Minsky
Henry Minsky is the son of artificial intelligence pioneer Marvin Minsky and is known as a software engineer and technologist.
-
C.
Ralph Guggenheim
Ralph Guggenheim is an American film producer best known for his work at Pixar, where he helped pioneer computer-animated feature filmmaking.
-
D.
Nathan Straus
Nathan Straus was a German-born American merchant and philanthropist best known as a co-owner of Macy’s and for pioneering public milk pasteurization programs to combat disease.
-
E.
Arthur Richman
Arthur Richman was an American playwright and screenwriter best known for his stage works that were adapted into successful films during the early 20th century.
- 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_69d87f1a8dd881909f1de6ef78849874 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff6b91a48190a04648d4cad2c4b1 |
completed | April 17, 2026, 9:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffeba1e4c08190a90f5102e0038056 |
completed | May 10, 2026, 2:21 a.m. |
Created at: April 10, 2026, 5 a.m.