Triple
T10620795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Larisa Latynina |
E250193
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Latynina |
E250193
|
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: Latynina | Statement: [Larisa Latynina, familyName, Latynina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Latynina Context triple: [Larisa Latynina, familyName, Latynina]
-
A.
Latynina
chosen
Latynina is a Russian surname most famously associated with Larisa Latynina, one of the most decorated gymnasts in Olympic history.
-
B.
Latn
Latn is the ISO 15924 script code representing the Latin alphabet used for writing numerous modern languages worldwide.
-
C.
Surzhyk
Surzhyk is a mixed sociolect that blends elements of Ukrainian and Russian, commonly spoken in various regions of Ukraine.
-
D.
Ruen
Ruen is a small town and administrative center in southeastern Bulgaria known for its ethnically diverse population and rural surroundings.
-
E.
Burúśaski
Burúśaski is a language isolate spoken primarily in northern Pakistan, notable for its unique grammatical structure and lack of proven relation to any other language family.
- 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_69d6aa5993448190a493b790b8f85010 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6df70b0288190bf6edd705632ff02 |
completed | April 8, 2026, 11:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d96b908e788190bc9e4f327e871a7f |
completed | April 10, 2026, 9:28 p.m. |
Created at: April 8, 2026, 8:49 p.m.