Triple
T10544545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Don DeFore |
E248780
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Don DeFore |
E248780
|
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: Don DeFore | Statement: [Don DeFore, name, Don DeFore]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Don DeFore Context triple: [Don DeFore, name, Don DeFore]
-
A.
Don DeFore
chosen
Don DeFore was an American film and television actor best known for his roles in mid-20th-century sitcoms, including playing friendly neighbor characters in popular series.
-
B.
Franchot Tone
Franchot Tone was an American stage and film actor of the 1930s and 1940s, known for his roles in movies like "Mutiny on the Bounty" and for being part of Hollywood's early star system.
-
C.
Sharon Reed
Sharon Reed is a visual effects industry professional best known as one of the founders of the renowned VFX and creative studio Framestore.
-
D.
Dina Merrill
Dina Merrill was an American actress, heiress, and philanthropist known for her elegant screen presence in mid-20th-century Hollywood films and television.
-
E.
Linda Darnell
Linda Darnell was an American film actress of the 1940s and 1950s, known for her beauty and roles in Hollywood classics such as "Forever Amber" and "A Letter to Three Wives."
- 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_69d381c733c08190ab1dd6239f5f34ae |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d519128cac819086c93f3bab854ac2 |
completed | April 7, 2026, 2:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbb6db3f2c81908a7cb28ca8e8ebc9 |
completed | April 12, 2026, 3:14 p.m. |
Created at: April 6, 2026, 12:32 p.m.