Triple
T17523146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Barbara Bel Geddes |
E426726
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Barbara Bel Geddes |
—
|
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: Barbara Bel Geddes | Statement: [Barbara Bel Geddes, name, Barbara Bel Geddes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Barbara Bel Geddes Context triple: [Barbara Bel Geddes, name, Barbara Bel Geddes]
-
A.
Barbara Bel Geddes
chosen
Barbara Bel Geddes was an American stage, film, and television actress best known for her role as Miss Ellie Ewing on the long-running TV series "Dallas."
-
B.
Nancy Richardson
Nancy Richardson is a film editor known for her work on notable movies including the drama "To Sleep with Anger."
-
C.
Nancy Richardson
Nancy Richardson is a film editor known for her work on major Hollywood productions, including the comedy heist movie "Tower Heist."
-
D.
Nancy Richardson
Nancy Richardson is a film editor best known for her work on movies such as the biographical sports comedy-drama "Fighting with My Family."
-
E.
Nancy Richardson
Nancy Richardson is a film editor known for her work on feature films, including editing the romantic drama "The Last Song."
- 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_69d889de677081909b22d2657b1f0292 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452d40ee08190b79d8e3d7f1b1272 |
completed | April 19, 2026, 3:58 a.m. |
Created at: April 10, 2026, 5:49 a.m.