Triple
T6428185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wendell Willkie |
E128111
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Willkie
Willkie is the surname most notably associated with Wendell Willkie, the 1940 Republican nominee for President of the United States.
|
E592445
|
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: Willkie | Statement: [Wendell Willkie, familyName, Willkie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Willkie Context triple: [Wendell Willkie, familyName, Willkie]
-
A.
Fulbright & Jaworski
Fulbright & Jaworski was a major U.S. law firm, known for its national and international practice, that later became part of Norton Rose Fulbright.
-
B.
Whitman Chambers
Whitman Chambers was an American screenwriter known for his work on mid-20th-century crime and mystery films.
-
C.
Pola Debevoise
Pola Debevoise is a glamorous, nearsighted fashion model portrayed by Marilyn Monroe in the 1953 romantic comedy film "How to Marry a Millionaire."
-
D.
Winston & Strawn
Winston & Strawn is a major international law firm headquartered in Chicago, known for its litigation, corporate, and regulatory practices.
-
E.
Osgood Perkins
Osgood Perkins was an American stage and film actor of the early 20th century, known for his character roles in Hollywood’s pre-Code era.
- 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: Willkie Triple: [Wendell Willkie, familyName, Willkie]
Generated description
Willkie is the surname most notably associated with Wendell Willkie, the 1940 Republican nominee for President of the United States.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Willkie Target entity description: Willkie is the surname most notably associated with Wendell Willkie, the 1940 Republican nominee for President of the United States.
-
A.
Fulbright & Jaworski
Fulbright & Jaworski was a major U.S. law firm, known for its national and international practice, that later became part of Norton Rose Fulbright.
-
B.
Whitman Chambers
Whitman Chambers was an American screenwriter known for his work on mid-20th-century crime and mystery films.
-
C.
Pola Debevoise
Pola Debevoise is a glamorous, nearsighted fashion model portrayed by Marilyn Monroe in the 1953 romantic comedy film "How to Marry a Millionaire."
-
D.
Winston & Strawn
Winston & Strawn is a major international law firm headquartered in Chicago, known for its litigation, corporate, and regulatory practices.
-
E.
Osgood Perkins
Osgood Perkins was an American stage and film actor of the early 20th century, known for his character roles in Hollywood’s pre-Code era.
- 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_69c00838de888190af2eec0b80495efa |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c06922a27881908c5571f2aa31e0c1 |
completed | March 22, 2026, 10:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c640e678608190b5a1dcd1076bc1f2 |
completed | March 27, 2026, 8:33 a.m. |
| NEDg | Description generation | batch_69c641d6024c8190996aae40851a3b73 |
completed | March 27, 2026, 8:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6425e0a348190bc1eb90eb8c00597 |
completed | March 27, 2026, 8:39 a.m. |
Created at: March 22, 2026, 4:44 p.m.