Triple
T14472902
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Janis |
E358888
|
entity |
| Predicate | canBeShortFormOf |
P32259
|
FINISHED |
| Object | Janis (Latvian: Jānis) |
E227690
|
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: Janis (Latvian: Jānis) | Statement: [Janis, canBeShortFormOf, Janis (Latvian: Jānis)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Janis (Latvian: Jānis) Context triple: [Janis, canBeShortFormOf, Janis (Latvian: Jānis)]
-
A.
Jāņi
Jāņi is a major Latvian midsummer festival celebrating the summer solstice with folk songs, bonfires, wreaths, and traditional foods.
-
B.
Jani Zubkovs
Jani Zubkovs is a music producer and bassist best known for his work with the band Drop Dead Gorgeous.
-
C.
Mihails
Mihails is a masculine given name, commonly used in Latvia and other Eastern European countries, that is cognate with names like Michael and Mikelis.
-
D.
Jaan
chosen
Jaan is a masculine given name commonly used in Estonia, equivalent to the English name John.
-
E.
Latka Gravas
Latka Gravas is a lovable, eccentric immigrant mechanic known for his childlike innocence and quirky speech on the sitcom "Taxi."
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de91fab21c819090b6e209d8efba6e |
completed | April 14, 2026, 7:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd649e103c81908001b45c16d1fd79 |
completed | May 8, 2026, 4:20 a.m. |
Created at: April 10, 2026, 1:20 a.m.