Triple
T21018185
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niki Karimi |
E517734
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Niki |
E807688
|
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: Niki | Statement: [Niki Karimi, givenName, Niki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Niki Context triple: [Niki Karimi, givenName, Niki]
-
A.
Niki
chosen
Niki is a given name that can be used for people of any gender in various cultures.
-
B.
Niki
Niki is a small town in Hokkaido, Japan, known for its fruit farming and rural scenery.
-
C.
Nikki
Nikki is a seductive and ambitious burlesque performer featured as one of the central characters in the musical film "Burlesque."
-
D.
Nikki
Nikki is the estranged wife of Pat Solitano in the film "Silver Linings Playbook," whose separation from him drives much of the movie’s emotional conflict.
-
E.
Nikki
Nikki is the central protagonist of the 1993 coming-of-age sports comedy film "Airborne," known for his laid-back California surfer attitude and exceptional inline skating skills.
- 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_69e0b50262b081909bc488937145eb73 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc5a27f08190b26828a6a7b59f7c |
completed | April 21, 2026, 4:26 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09473f46b48190a7dd1ef79a4c24ce |
completed | May 17, 2026, 4:42 a.m. |
Created at: April 16, 2026, 1:54 p.m.