Triple
T97621
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Kong (1933 film) |
E1966
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object | Ann Darrow |
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: Ann Darrow | Statement: [King Kong (1933 film), featuresCharacter, Ann Darrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ann Darrow Context triple: [King Kong (1933 film), featuresCharacter, Ann Darrow]
-
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.
Sara Ann Delano
Sara Ann Delano was an American socialite and the mother of U.S. President Franklin D. Roosevelt.
-
C.
Lucille Sheardown
Lucille Sheardown was one of the later wives of American inventor Lee de Forest, associated with his personal life rather than his pioneering work in radio and electronics.
-
D.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
E.
Anna
Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
- 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_69a24d4862f881908cc8b89d3a78031d |
completed | Feb. 28, 2026, 2:04 a.m. |
| NER | Named-entity recognition | batch_69a24feef1b08190bb9525f71cce053e |
completed | Feb. 28, 2026, 2:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2aa43d2948190bc7ec06d157fab9a |
completed | Feb. 28, 2026, 8:41 a.m. |
Created at: Feb. 28, 2026, 2:09 a.m.