Triple
T10818681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Larry Keller |
E255301
|
entity |
| Predicate | mother |
P120
|
FINISHED |
| Object | Kate Keller |
E144523
|
NE FINISHED |
How this triple was built (2 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 Keller | Statement: [Larry Keller, mother, Kate Keller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Keller Context triple: [Larry Keller, mother, Kate Keller]
-
A.
Kate Keller
chosen
Kate Keller is a central character in Arthur Miller's play "All My Sons," portrayed as a mother in deep denial about her missing son and the moral failures of her family.
-
B.
Rachel Keller
Rachel Keller is an American actress known for her roles in television series such as Fargo, Legion, and Tokyo Vice.
-
C.
Rose Loomis
Rose Loomis is the seductive and scheming wife portrayed by Marilyn Monroe in the 1953 film noir "Niagara."
-
D.
Claudia Hollingsworth
Claudia Hollingsworth is a competitive swimmer recognized for representing New Zealand at the international level.
-
E.
Lila Norcross
Lila Norcross is a central protagonist in Stephen King and Owen King’s novel "Sleeping Beauties," serving as a key figure navigating the chaos that erupts when women around the world fall into a mysterious sleep.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7344866f88190be4addb7c8020fce |
completed | April 9, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de8569178481909474e939a3e4c217 |
completed | April 14, 2026, 6:20 p.m. |
Created at: April 8, 2026, 9:18 p.m.