Triple
T4939209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warren |
E110885
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Leslie Warren
Leslie Warren is an American actress known for her work in film, television, and stage productions.
|
E485487
|
NE FINISHED |
How this triple was built (4 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: Leslie Warren | Statement: [Warren, hasNotableBearer, Leslie Warren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leslie Warren Context triple: [Warren, hasNotableBearer, Leslie Warren]
-
A.
Myrna Dell
Myrna Dell was an American film and television actress known for her roles in 1940s and 1950s Hollywood productions, particularly in film noir and B-movies.
-
B.
Lucille La Verne
Lucille La Verne was an American stage and film actress best remembered for providing the voice of the Evil Queen in Disney’s classic animated film "Snow White and the Seven Dwarfs."
-
C.
Myrna Fahey
Myrna Fahey was an American actress known for her film and television roles in the 1950s and 1960s, often appearing in comedies and dramas.
-
D.
Leslie Harter
Leslie Harter is a film producer known for her work in Hollywood and for being married to director Robert Zemeckis.
-
E.
Lela Rogers
Lela Rogers was an American journalist, screenwriter, and acting coach best known as the mother and early career mentor of Hollywood star Ginger Rogers.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Leslie Warren Triple: [Warren, hasNotableBearer, Leslie Warren]
Generated description
Leslie Warren is an American actress known for her work in film, television, and stage productions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Leslie Warren Target entity description: Leslie Warren is an American actress known for her work in film, television, and stage productions.
-
A.
Myrna Dell
Myrna Dell was an American film and television actress known for her roles in 1940s and 1950s Hollywood productions, particularly in film noir and B-movies.
-
B.
Lucille La Verne
Lucille La Verne was an American stage and film actress best remembered for providing the voice of the Evil Queen in Disney’s classic animated film "Snow White and the Seven Dwarfs."
-
C.
Myrna Fahey
Myrna Fahey was an American actress known for her film and television roles in the 1950s and 1960s, often appearing in comedies and dramas.
-
D.
Leslie Harter
Leslie Harter is a film producer known for her work in Hollywood and for being married to director Robert Zemeckis.
-
E.
Lela Rogers
Lela Rogers was an American journalist, screenwriter, and acting coach best known as the mother and early career mentor of Hollywood star Ginger Rogers.
- F. None of above. chosen
Provenance (5 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7088f6e48190bf09e58ab053a4d1 |
completed | March 20, 2026, 4:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be89eb6cac8190b82bef41062fc8f1 |
completed | March 21, 2026, 12:07 p.m. |
| NEDg | Description generation | batch_69be8e2f9ed48190b05b4cb4f173a501 |
completed | March 21, 2026, 12:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69be8e7a52a081908910686229c42745 |
completed | March 21, 2026, 12:26 p.m. |
Created at: March 20, 2026, 1:31 p.m.