Triple
T15245947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Betty Lou Gerson |
E364379
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Betty Lou Gerson |
E364379
|
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: Betty Lou Gerson | Statement: [Betty Lou Gerson, name, Betty Lou Gerson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betty Lou Gerson Context triple: [Betty Lou Gerson, name, Betty Lou Gerson]
-
A.
Betty Lou Gerson
chosen
Betty Lou Gerson was an American actress best known for voicing the villainous Cruella de Vil in Disney’s animated classic "One Hundred and One Dalmatians."
-
B.
Audrey Totter
Audrey Totter was an American film and television actress best known for her tough, alluring roles in classic 1940s film noir.
-
C.
Lucille Bliss
Lucille Bliss was an American voice actress best known for her work in classic animated films and television, including early Disney productions and the original Smurfs series.
-
D.
Louise Glaum
Louise Glaum was a prominent American silent film actress of the 1910s and early 1920s, best known for her sophisticated "vamp" roles in melodramas.
-
E.
Shalom Harlow
Shalom Harlow is a Canadian model and actress known for her work in high-fashion modeling and roles in films such as "How to Lose a Guy in 10 Days."
- 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_69d85a0dde7481908fc64d1e82d5d20d |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e007f306f08190be448b215d6c9b6c |
completed | April 15, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007581ba008190a6d558c8f4e861d6 |
completed | May 10, 2026, 12:09 p.m. |
Created at: April 10, 2026, 3:13 a.m.