Triple
T6718102
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Georgia Hale |
E153323
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Georgia Hale |
E153323
|
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: Georgia Hale | Statement: [Georgia Hale, name, Georgia Hale]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Georgia Hale Context triple: [Georgia Hale, name, Georgia Hale]
-
A.
Georgia Hale
chosen
Georgia Hale was an American silent film actress best known for her role opposite Charlie Chaplin in the classic 1925 film "The Gold Rush."
-
B.
Zelma Atwood
Zelma Atwood is best known as the widow of legendary soul singer Otis Redding and the longtime steward of his musical legacy and estate.
-
C.
Sarah Horton
Sarah Horton was the wife of early American colonist and Salem leader Roger Conant, associated with the founding period of Salem, Massachusetts.
-
D.
Nettie Fowler
Nettie Fowler is a central character in the Rodgers and Hammerstein musical "Carousel," known as a strong, nurturing figure who helps guide the story’s emotional core.
-
E.
Bertha Perkins
Bertha Perkins is one of the daughters of renowned American book editor Maxwell Perkins.
- 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_69c68809b4608190a2509ddb5ab87f05 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d12765a48190b485176dc2ffa0fa |
completed | March 27, 2026, 6:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7009b9b64819095ae1a65cd72c374 |
completed | March 27, 2026, 10:11 p.m. |
Created at: March 27, 2026, 2:07 p.m.