Triple
T5143246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wonderland Sound and Vision |
E116009
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Nikita |
E498068
|
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: Nikita | Statement: [Wonderland Sound and Vision, notableWork, Nikita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nikita Context triple: [Wonderland Sound and Vision, notableWork, Nikita]
-
A.
Nikita
Nikita is a given name most famously associated with Soviet leader Nikita Khrushchev.
-
B.
Nikita
chosen
Nikita is an American action-thriller television series centered on a rogue assassin seeking to dismantle the secret government organization that trained her.
-
C.
Nikkiya
Nikkiya is an American singer and rapper known for her collaborations in hip-hop and R&B, particularly with producer and artist K.E. on the Track (Keys).
-
D.
Nikita Dragun
Nikita Dragun is a transgender beauty influencer, YouTuber, and entrepreneur known for her makeup content and cosmetics brand Dragun Beauty.
-
E.
Nikki
Nikki is a seductive and ambitious burlesque performer featured as one of the central characters in the musical film "Burlesque."
- 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_69bd4446c0e08190a7c29dc74976bf03 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7883004881909c763da818d9b6e2 |
completed | March 20, 2026, 4:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed91e4ab88190827a77b0a356b7c3 |
completed | March 21, 2026, 5:45 p.m. |
Created at: March 20, 2026, 1:43 p.m.