Triple
T3982775
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Behrens |
E86796
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Betty Behrens
Betty Behrens was a British historian known for her influential work on early modern European history and economic institutions.
|
E408148
|
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: Betty Behrens | Statement: [Behrens, hasNotableBearer, Betty Behrens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Betty Behrens Context triple: [Behrens, hasNotableBearer, Betty Behrens]
-
A.
Betty Wold Johnson
Betty Wold Johnson was an American philanthropist and arts patron closely associated with the Johnson & Johnson family legacy.
-
B.
Betty Furness
Betty Furness was an American actress and television personality best known for her film roles in the 1930s and later as a pioneering consumer affairs advocate on TV.
-
C.
Betty Lou Keim
Betty Lou Keim was an American film and television actress best known for her roles in 1950s teen dramas and coming-of-age stories.
-
D.
Betty Dahl
Betty Dahl was the wife of influential American political scientist Robert A. Dahl.
-
E.
Betty Bronson
Betty Bronson was an American film actress best known for her roles in silent and early sound films, including her iconic portrayal of Peter Pan in the 1924 adaptation.
- 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: Betty Behrens Triple: [Behrens, hasNotableBearer, Betty Behrens]
Generated description
Betty Behrens was a British historian known for her influential work on early modern European history and economic institutions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Betty Behrens Target entity description: Betty Behrens was a British historian known for her influential work on early modern European history and economic institutions.
-
A.
Betty Wold Johnson
Betty Wold Johnson was an American philanthropist and arts patron closely associated with the Johnson & Johnson family legacy.
-
B.
Betty Furness
Betty Furness was an American actress and television personality best known for her film roles in the 1930s and later as a pioneering consumer affairs advocate on TV.
-
C.
Betty Lou Keim
Betty Lou Keim was an American film and television actress best known for her roles in 1950s teen dramas and coming-of-age stories.
-
D.
Betty Dahl
Betty Dahl was the wife of influential American political scientist Robert A. Dahl.
-
E.
Betty Bronson
Betty Bronson was an American film actress best known for her roles in silent and early sound films, including her iconic portrayal of Peter Pan in the 1924 adaptation.
- 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_69aed93fd9d4819085d3b2137d2346cb |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef9dd351c81909605bc2605f541e1 |
completed | March 9, 2026, 4:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5561f0a2881909d758a8fba58309d |
completed | March 14, 2026, 12:35 p.m. |
| NEDg | Description generation | batch_69b5575b1f748190b91f5f1cb4cf9c8b |
completed | March 14, 2026, 12:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b557d445d081908f48fe3bd06f786e |
completed | March 14, 2026, 12:43 p.m. |
Created at: March 9, 2026, 3:33 p.m.