Triple
T16770875
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gail Berman |
E407590
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Gail Berman |
—
|
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: Gail Berman | Statement: [Gail Berman, name, Gail Berman]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gail Berman Context triple: [Gail Berman, name, Gail Berman]
-
A.
Gail Berman
chosen
Gail Berman is an American television and film producer and media executive known for her influential roles at major studios and for producing high-profile projects across network TV and Hollywood.
-
B.
Gail Berke
Gail Berke is a central protagonist in the adventure film "The Deep," known for becoming entangled in a dangerous underwater treasure hunt.
-
C.
Gail Katz
Gail Katz is an American film and television producer known for working on major Hollywood projects including the disaster drama "The Perfect Storm."
-
D.
Lynn Grossman
Lynn Grossman is the wife of American actor and director Bob Balaban.
-
E.
Jill Krementz
Jill Krementz is an American photographer and author best known for her portraits of writers and her work in children's literature.
- 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_69d8839174188190909f190097207065 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b0361e5081908f58edf766ff3ce0 |
completed | April 18, 2026, 4:24 p.m. |
Created at: April 10, 2026, 5:21 a.m.