Triple
T14040606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiya Tomlin |
E337834
|
entity |
| Predicate | brandName |
P1500
|
FINISHED |
| Object | Kiya Tomlin |
E337834
|
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: Kiya Tomlin | Statement: [Kiya Tomlin, brandName, Kiya Tomlin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kiya Tomlin Context triple: [Kiya Tomlin, brandName, Kiya Tomlin]
-
A.
Kiya Tomlin
chosen
Kiya Tomlin is an American fashion designer and entrepreneur known for her eponymous clothing line and custom womenswear.
-
B.
Kacey Rohl
Kacey Rohl is a Canadian actress known for her roles in television series such as Hannibal, The Magicians, and Arrow, as well as various film and TV projects.
-
C.
Melissa Ann Montgomery
Melissa Ann Montgomery is known as the daughter of American actor and director George Montgomery.
-
D.
Lisa McNear
Lisa McNear was an American artist and socialite best known as the mother of television host and political commentator Tucker Carlson.
-
E.
Rya Kihlstedt
Rya Kihlstedt is an American actress known for her work in film and television, including prominent roles in series such as "A Teacher."
- 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_69d81c664e48819088cbd8f433aeffe5 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de311814e48190adb637e1c97c0658 |
completed | April 14, 2026, 12:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbc33dbc8c819080b6cb3d589da7a1 |
completed | May 6, 2026, 10:39 p.m. |
Created at: April 9, 2026, 10:20 p.m.