Triple
T5092799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miquel |
E114791
|
entity |
| Predicate | hasEquivalentName |
P3889
|
FINISHED |
| Object | Mikkel |
E99056
|
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: Mikkel | Statement: [Miquel, hasEquivalentName, Mikkel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mikkel Context triple: [Miquel, hasEquivalentName, Mikkel]
-
A.
Mikael
chosen
Mikael is a masculine given name commonly used in Scandinavian and Finnish cultures, equivalent to Michael.
-
B.
Jørgen
Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
-
C.
Mikkel Eriksen
Mikkel Eriksen is a Norwegian record producer and songwriter best known as one half of the production duo Stargate, which has crafted numerous global pop and R&B hits.
-
D.
Mikkel Svane
Mikkel Svane is a Danish entrepreneur best known as the co-founder and longtime CEO of the customer service software company Zendesk.
-
E.
Morten
Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
- 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_69bd443fc49c819089629c00e311310c |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd754369708190bf4e171a904a19e1 |
completed | March 20, 2026, 4:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bec360a5848190a243da780b53559c |
completed | March 21, 2026, 4:12 p.m. |
Created at: March 20, 2026, 1:40 p.m.