Triple
T7747018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | April Kepner |
E175655
|
entity |
| Predicate | hasSibling |
P363
|
FINISHED |
| Object | Libby Kepner |
E686867
|
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: Libby Kepner | Statement: [April Kepner, hasSibling, Libby Kepner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Libby Kepner Context triple: [April Kepner, hasSibling, Libby Kepner]
-
A.
Libby Snyder
Libby Snyder is known as the spouse of American poet James Wright.
-
B.
Libby Geist
Libby Geist is an American documentary film producer best known for her work on acclaimed sports and social-issue documentaries, including the Oscar-winning "O.J.: Made in America."
-
C.
Natalie Keener
Natalie Keener is a young, ambitious corporate downsizing consultant in the film "Up in the Air," whose modern, efficiency-driven outlook clashes with and ultimately reshapes Ryan Bingham’s worldview.
-
D.
Libby Leist
Libby Leist is a television news executive best known for her leadership role as an executive producer at NBC’s "Today" show.
-
E.
Kimmie Kepner
chosen
Kimmie Kepner is a minor fictional character in the Grey's Anatomy universe, known primarily as the sister of trauma surgeon April Kepner.
- 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_69c69960b3588190a53aa590d31d9544 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7038caa64819084f61b42a73c2d8b |
completed | March 27, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbde8e8264819082a3954072dffd09 |
completed | March 31, 2026, 2:47 p.m. |
Created at: March 27, 2026, 4:08 p.m.