Triple
T5066458
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ma Barker |
E114155
|
entity |
| Predicate | alternateName |
P39
|
FINISHED |
| Object |
Kate Barker
Kate Barker, better known as Ma Barker, was an American criminal figure infamous for leading the Barker–Karpis gang during the early 20th century.
|
E490030
|
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: Kate Barker | Statement: [Ma Barker, alternateName, Kate Barker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Barker Context triple: [Ma Barker, alternateName, Kate Barker]
-
A.
Sophie Hunter
Sophie Hunter is a British theatre and opera director, playwright, and former actress known for her avant-garde stage work and marriage to actor Benedict Cumberbatch.
-
B.
Jennifer Bourke
Jennifer Bourke is known as the spouse of actor Robert Shaw.
-
C.
Kate Lynch
Kate Lynch is a Canadian actress best known for her role in the 1979 comedy film "Meatballs."
-
D.
Katherine Wilkinson
Katherine Wilkinson is a climate strategist, author, and speaker known for her work on solutions-focused climate communication and leadership, including co-editing the influential book "All We Can Save."
-
E.
Rebecca Yeldham
Rebecca Yeldham is a film producer known for her work on acclaimed independent and international films, including the adaptation of "The Kite Runner."
- 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: Kate Barker Triple: [Ma Barker, alternateName, Kate Barker]
Generated description
Kate Barker, better known as Ma Barker, was an American criminal figure infamous for leading the Barker–Karpis gang during the early 20th century.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kate Barker Target entity description: Kate Barker, better known as Ma Barker, was an American criminal figure infamous for leading the Barker–Karpis gang during the early 20th century.
-
A.
Sophie Hunter
Sophie Hunter is a British theatre and opera director, playwright, and former actress known for her avant-garde stage work and marriage to actor Benedict Cumberbatch.
-
B.
Jennifer Bourke
Jennifer Bourke is known as the spouse of actor Robert Shaw.
-
C.
Kate Lynch
Kate Lynch is a Canadian actress best known for her role in the 1979 comedy film "Meatballs."
-
D.
Katherine Wilkinson
Katherine Wilkinson is a climate strategist, author, and speaker known for her work on solutions-focused climate communication and leadership, including co-editing the influential book "All We Can Save."
-
E.
Rebecca Yeldham
Rebecca Yeldham is a film producer known for her work on acclaimed independent and international films, including the adaptation of "The Kite Runner."
- 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_69bd443c0c8c81908663b77afb28e165 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd749aceac8190817278266308fd64 |
completed | March 20, 2026, 4:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bea49d917081909ead17eed3f8af90 |
completed | March 21, 2026, 2:01 p.m. |
| NEDg | Description generation | batch_69bea525d9088190b0b655687dd27630 |
completed | March 21, 2026, 2:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bea594850881909cd683670b63a079 |
completed | March 21, 2026, 2:05 p.m. |
Created at: March 20, 2026, 1:38 p.m.