Triple
T15991694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mikki Kunttu |
E387844
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mikki |
E883709
|
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: Mikki | Statement: [Mikki Kunttu, givenName, Mikki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mikki Context triple: [Mikki Kunttu, givenName, Mikki]
-
A.
Mikki
chosen
Mikki is a supporting character in the romantic comedy film "Chalet Girl," set in the world of upscale Alpine ski resorts.
-
B.
Michi
Michi is a diminutive or affectionate nickname commonly used for the given name Michiel.
-
C.
Michu
Michu is a key fictional character in Honoré de Balzac’s novel "Une ténébreuse affaire," notable for his involvement in the book’s political and judicial intrigues.
-
D.
Michu
Michu is a retired Spanish attacking midfielder and forward best known for his prolific 2012–13 season with Swansea City in the English Premier League.
-
E.
Mici
Mici is the given name of Mária Harkányi, a person known by this shorter personal name.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157844ed881908b42bfc1bb740d4e |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3d3ef2881909213ff608192f1ef |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:54 a.m.