Triple
T1154510
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kimberly |
E23752
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Kimberlee |
E23752
|
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: Kimberlee | Statement: [Kimberly, hasVariant, Kimberlee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kimberlee Context triple: [Kimberly, hasVariant, Kimberlee]
-
A.
Kimberly
chosen
Kimberly is a feminine given name of English origin that has been widely used in the United States since the mid-20th century.
-
B.
Kori Rae
Kori Rae is a film producer best known for her work at Pixar Animation Studios, including producing the animated feature "Monsters University."
-
C.
Korina
Korina is the surname of Irina Korina, a contemporary Russian artist known for her installations and sculptural works.
-
D.
Loralai
Loralai is a town and district in northern Balochistan, Pakistan, known historically as a regional administrative and trade center.
-
E.
Shira
Shira is the eroded western volcanic cone and plateau of Mount Kilimanjaro, forming one of the mountain’s three main summits.
- 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_69a493f0d32c8190ac74bad3c87f2641 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc8fbb548190865b1bf019f2bde4 |
completed | March 1, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac7642ff0c81909b323ac328b18e2e |
completed | March 7, 2026, 7:02 p.m. |
Created at: March 1, 2026, 7:44 p.m.