Triple
T22161007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red |
E547667
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Galina |
—
|
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: Galina | Statement: [Red, givenName, Galina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Galina Context triple: [Red, givenName, Galina]
-
A.
Galina
chosen
Galina is a feminine given name of Slavic origin, commonly used in Russia and other Eastern European countries.
-
B.
Ludmilla
Ludmilla is a coastal suburb of Darwin in Australia's Northern Territory, known for its residential areas and proximity to Fannie Bay.
-
C.
Ludmila
Ludmila is the heroine of Alexander Pushkin’s narrative poem "Ruslan and Ludmila," known as a beautiful Kievan princess whose abduction sets the story’s adventurous plot in motion.
-
D.
Lyudmila
Lyudmila is a common Russian female given name, notably borne by figures such as Soviet World War II sniper Lyudmila Pavlichenko.
-
E.
Lyudmila
Lyudmila is a Russian linguist and the former First Lady of Russia, known for being the ex-wife of President Vladimir Putin.
- 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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a2d8064819094d27ef9f15c6a1f |
completed | April 28, 2026, 9:44 p.m. |
Created at: April 16, 2026, 8:33 p.m.