Triple
T6590095
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steven B. Sample |
E159330
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Kathryn Brunkow Sample
Kathryn Brunkow Sample is best known as the wife of the late Steven B. Sample, the longtime president of the University of Southern California.
|
E603427
|
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: Kathryn Brunkow Sample | Statement: [Steven B. Sample, spouse, Kathryn Brunkow Sample]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kathryn Brunkow Sample Context triple: [Steven B. Sample, spouse, Kathryn Brunkow Sample]
-
A.
Kathryn Land
Kathryn Land is a fictional character appearing in the classic American film "Andy Hardy’s Private Secretary."
-
B.
Elizabeth Kolb
Elizabeth Kolb was the woman who served as the ceremonial sponsor for the U.S. Navy battleship USS Pennsylvania (BB-38) at its launching.
-
C.
Anna Kuhn
Anna Kuhn was the mother of Nobel Prize–winning theoretical physicist Hans Bethe.
-
D.
Sarah Brunsden
Sarah Brunsden was the wife of Sir John Copley, a British legal figure who served as Lord Chancellor in the early 19th century.
-
E.
Kathryn
Kathryn is a feminine given name, commonly considered a variant spelling of Katherine/Catherine.
- 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: Kathryn Brunkow Sample Triple: [Steven B. Sample, spouse, Kathryn Brunkow Sample]
Generated description
Kathryn Brunkow Sample is best known as the wife of the late Steven B. Sample, the longtime president of the University of Southern California.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kathryn Brunkow Sample Target entity description: Kathryn Brunkow Sample is best known as the wife of the late Steven B. Sample, the longtime president of the University of Southern California.
-
A.
Kathryn Land
Kathryn Land is a fictional character appearing in the classic American film "Andy Hardy’s Private Secretary."
-
B.
Elizabeth Kolb
Elizabeth Kolb was the woman who served as the ceremonial sponsor for the U.S. Navy battleship USS Pennsylvania (BB-38) at its launching.
-
C.
Anna Kuhn
Anna Kuhn was the mother of Nobel Prize–winning theoretical physicist Hans Bethe.
-
D.
Sarah Brunsden
Sarah Brunsden was the wife of Sir John Copley, a British legal figure who served as Lord Chancellor in the early 19th century.
-
E.
Kathryn
Kathryn is a feminine given name, commonly considered a variant spelling of Katherine/Catherine.
- 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_69c688366ce8819083f8883983c0df92 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6aeb201e88190808cf5779349f96c |
completed | March 27, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d57bde388190919ff6820e1b9610 |
completed | March 27, 2026, 7:07 p.m. |
| NEDg | Description generation | batch_69c6d6adc8c88190aa4ed066a2c99657 |
completed | March 27, 2026, 7:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6d8486534819080b75cad9cc32276 |
completed | March 27, 2026, 7:19 p.m. |
Created at: March 27, 2026, 1:55 p.m.