Triple
T37192
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Nance Garner |
E735
|
entity |
| Predicate | middleName |
P143
|
FINISHED |
| Object |
Nance
Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
|
E9716
|
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: Nance | Statement: [John Nance Garner, middleName, Nance]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nance Context triple: [John Nance Garner, middleName, Nance]
-
A.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
B.
Anna
Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
-
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.
Emma Savage Rogers
Emma Savage Rogers was the wife of William Barton Rogers, the 19th-century American geologist and founder of the Massachusetts Institute of Technology.
-
E.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
- 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: Nance Triple: [John Nance Garner, middleName, Nance]
Generated description
Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nance Target entity description: Nance is the middle name of John Nance Garner, the 32nd vice president of the United States under Franklin D. Roosevelt.
-
A.
Barbara
Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
-
B.
Anna
Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
-
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.
Emma Savage Rogers
Emma Savage Rogers was the wife of William Barton Rogers, the 19th-century American geologist and founder of the Massachusetts Institute of Technology.
-
E.
Louise
Louise is a feminine given name of French origin, traditionally associated with nobility and widely used in many European and English-speaking countries.
- 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_69a247a8f6c08190bac804906d62ed5a |
completed | Feb. 28, 2026, 1:40 a.m. |
| NER | Named-entity recognition | batch_69a24acbb90881908c9f77e74034eb52 |
completed | Feb. 28, 2026, 1:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a26c15db808190b8d66206a4ed1085 |
completed | Feb. 28, 2026, 4:16 a.m. |
| NEDg | Description generation | batch_69a26da8de008190872009f99d1aa684 |
completed | Feb. 28, 2026, 4:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a26e45509c8190953e288c45924734 |
completed | Feb. 28, 2026, 4:25 a.m. |
Created at: Feb. 28, 2026, 1:46 a.m.