Triple
T383160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine |
E8723
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Katia |
E61396
|
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: Katia | Statement: [Catherine, hasVariant, Katia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katia Context triple: [Catherine, hasVariant, Katia]
-
A.
Katya
chosen
Katya is a diminutive and affectionate form of the given name Catherine, commonly used in Slavic and other European cultures.
-
B.
Paola
Paola is an Italian noblewoman who became Queen consort of Belgium as the wife of King Albert II.
-
C.
Mila
Mila is a leading artificial intelligence research institute based in Quebec, renowned for its work in deep learning and machine learning.
-
D.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
E.
Rita
Rita is a feminine given name used in various cultures, often as a short form of names like Margarita.
- 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_69a2e7f47dd08190a4e294ccbbe46cd4 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ec40ff8c81909306eb2dfe1512af |
completed | Feb. 28, 2026, 1:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4c6698344819084b55d4a50408e5d |
completed | March 1, 2026, 11:06 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.