Triple
T12069433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nicki Collen |
E287383
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Nicki |
E287382
|
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: Nicki | Statement: [Nicki Collen, givenName, Nicki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nicki Context triple: [Nicki Collen, givenName, Nicki]
-
A.
Nicki
chosen
Nicki is a professional basketball coach best known for her work in women's basketball, including in the WNBA and NCAA.
-
B.
Nicki Aycox
Nicki Aycox was an American actress and musician best known for her roles in television series like "Supernatural" and films such as "Jeepers Creepers 2."
-
C.
Prince Nicki
Prince Nicki is a fictional royal character appearing in the animated short film "The Wedding March."
-
D.
Nicki Minaj
Nicki Minaj is a Trinidadian-born American rapper, singer, and songwriter known for her animated flow, alter egos, and major influence on contemporary hip hop and pop music.
-
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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90457fd488190b311ed69d2aebdf9 |
completed | April 10, 2026, 2:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f65af0e88190ad32adb9ff76b01b |
completed | May 2, 2026, 1:04 p.m. |
Created at: April 8, 2026, 9:48 p.m.