Triple
T20200398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George IV of Georgia |
E493200
|
entity |
| Predicate | otherName |
P39
|
FINISHED |
| Object | Lasha-George |
—
|
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: Lasha-George | Statement: [George IV of Georgia, otherName, Lasha-George]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lasha-George Context triple: [George IV of Georgia, otherName, Lasha-George]
-
A.
Lasha-George
chosen
Lasha-George was a medieval Georgian king, known formally as George IV of Georgia, who ruled during the early 13th century and continued the legacy of the Georgian Golden Age.
-
B.
Georgy
Georgy is a masculine given name of Russian origin, notably borne by Soviet military commander Georgy Zhukov.
-
C.
Dorla Gondi
Dorla Gondi is a regional dialect of the Gondi language spoken by the Dorla subgroup of the Gondi people in central India.
-
D.
Gela Nash
Gela Nash is an American fashion designer and co-founder of the clothing brand Juicy Couture.
-
E.
Chete Lera
Chete Lera was a Spanish actor known for his work in film, television, and theater, including roles in acclaimed movies of the 1990s and 2000s.
- 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_69da6269614c8190bb40475d9d477358 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66d8d01648190b1b3a6e03f0258d8 |
completed | April 20, 2026, 6:16 p.m. |
Created at: April 11, 2026, 11:37 p.m.