Triple
T13410321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mykyta |
E320068
|
entity |
| Predicate | hasDiminutiveForm |
P456
|
FINISHED |
| Object | Myko |
E622383
|
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: Myko | Statement: [Mykyta, hasDiminutiveForm, Myko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Myko Context triple: [Mykyta, hasDiminutiveForm, Myko]
-
A.
Myko
chosen
Myko is a short, informal given name or nickname derived from the Slavic name Mykola.
-
B.
Gomba
Gomba is a district within the Buganda region of central Uganda, known primarily for its rural communities and agricultural activities.
-
C.
Myus
Myus was an ancient Greek city of Ionia located near the mouth of the Maeander River in western Anatolia.
-
D.
Hongo
Hongo is a historic district in Bunkyo, Tokyo, known for its academic institutions, including the main campus of the University of Tokyo.
-
E.
Mykelti
Mykelti is the distinctive given name of American actor Mykelti Williamson, known for roles in films like "Forrest Gump" and various television series.
- 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_69d806b943cc8190b6af624d385d7e12 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaeb3facc819088c1af3b59237e7a |
completed | April 12, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7307ccff08190aa4037aa5a48f7d0 |
completed | May 3, 2026, 11:24 a.m. |
Created at: April 9, 2026, 9:35 p.m.