Triple
T23140296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Words |
E577441
|
entity |
| Predicate | director |
P255
|
FINISHED |
| Object | Brian Klugman |
—
|
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: Brian Klugman | Statement: [The Words, director, Brian Klugman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brian Klugman Context triple: [The Words, director, Brian Klugman]
-
A.
Brian Klugman
chosen
Brian Klugman is an American actor, screenwriter, and director known for co-writing films such as Tron: Legacy and The Words.
-
B.
Leon Feldhendler
Leon Feldhendler was a Polish Jewish resistance leader and Holocaust survivor best known for co-organizing the 1943 prisoner uprising at the Sobibor extermination camp.
-
C.
Mort Engelberg
Mort Engelberg is an American film producer and production manager known for his work on movies such as the 1980 thriller "Inferno."
-
D.
Phil Feldman
Phil Feldman was an American film producer best known for his work on influential Hollywood films of the 1960s and 1970s.
-
E.
Myron Futterman
Myron Futterman was an American businessman best known for being the first husband of actress Jane Wyman.
- 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_69e245f8e6248190ba3d58e068b4dccb |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18ec922b481908084eee6a95aef83 |
completed | April 29, 2026, 4:53 a.m. |
Created at: April 17, 2026, 4 p.m.