Triple
T201144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fay Wray |
E4506
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Fay Wray |
E4506
|
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: Fay Wray | Statement: [Fay Wray, name, Fay Wray]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fay Wray Context triple: [Fay Wray, name, Fay Wray]
-
A.
Fay Wray
chosen
Fay Wray was a Canadian-American actress best known for her iconic role as the damsel Ann Darrow in the classic 1933 film "King Kong."
-
B.
Veronica Lake
Veronica Lake was a popular American film actress of the 1940s, famed for her roles in film noir and her iconic peek-a-boo hairstyle.
-
C.
Kathleen Courtney
Kathleen Courtney is the birth name of Kathy Hochul, the 57th governor of New York and the first woman to hold that office.
-
D.
Joan Crawford
Joan Crawford was a legendary American film actress and Hollywood star whose career spanned from the silent era to the 1970s, earning her an Academy Award and enduring icon status.
-
E.
Elsa Lanchester
Elsa Lanchester was a British-born character actress best known for her eccentric and memorable roles in classic Hollywood films, including her iconic turn in "The Bride of Frankenstein."
- 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_69a25737567c81908f9c505300239181 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25be47ea881909c296b30a0d47a65 |
completed | Feb. 28, 2026, 3:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3836ea55c81909135f5f061e47da5 |
completed | March 1, 2026, 12:08 a.m. |
Created at: Feb. 28, 2026, 2:51 a.m.