Triple
T23096690
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mihály |
E575909
|
entity |
| Predicate | relatedName |
P3889
|
FINISHED |
| Object | Mikkel |
—
|
NE NERFINISHED |
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: Mikkel | Statement: [Mihály, relatedName, Mikkel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mikkel Context triple: [Mihály, relatedName, Mikkel]
-
A.
Mads
Mads is a Scandinavian given name commonly used for males, particularly in Denmark and Norway.
-
B.
Mads Dittmann Mikkelsen
Mads Dittmann Mikkelsen is a Danish actor renowned for his versatile performances in films and television series such as "Casino Royale," "Hannibal," and "Another Round."
-
C.
Mikkel Kessler
chosen
Mikkel Kessler is a Danish former professional boxer and multiple-time super middleweight world champion known for his technical skill and powerful jab.
-
D.
Mikael
Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
-
E.
Jørgen
Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e245c060b48190a9bd61a47a16db17 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18de522e48190a37e6c2fda2de465 |
completed | April 29, 2026, 4:49 a.m. |
Created at: April 17, 2026, 3:57 p.m.