Triple
T15584440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trumbo |
E374580
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Alan Baumgarten |
—
|
NE NERFINISHED |
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: Alan Baumgarten | Statement: [Trumbo, editedBy, Alan Baumgarten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alan Baumgarten Context triple: [Trumbo, editedBy, Alan Baumgarten]
-
A.
Alan Baumgarten
chosen
Alan Baumgarten is an American film editor known for his work on a variety of feature films and television projects.
-
B.
Craig Baumgarten
Craig Baumgarten is an American film producer known for working on action and drama movies, including mainstream Hollywood releases.
-
C.
Michael Feldman
Michael Feldman is an American political and communications strategist best known as the husband of television journalist Savannah Guthrie.
-
D.
Eric Tannenbaum
Eric Tannenbaum is a television producer best known for his work on popular American sitcoms, including serving as an executive producer on "Two and a Half Men."
-
E.
Stan Gottlieb
Stan Gottlieb is an actor best known for his role in the 1969 satirical film "Putney Swope."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d85ccd575081908909b71a3f3e3a61 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04e47971481909e986dd999354628 |
completed | April 16, 2026, 2:49 a.m. |
Created at: April 10, 2026, 4:11 a.m.