Triple
T19169807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kvareli Municipality |
E469288
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Gremi |
—
|
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: Gremi | Statement: [Kvareli Municipality, hasSettlement, Gremi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gremi Context triple: [Kvareli Municipality, hasSettlement, Gremi]
-
A.
Gremi
chosen
Gremi is a historic town and former royal residence in eastern Georgia’s Kakheti region, known for its 16th-century church and fortress complex.
-
B.
Gemer
Gemer is a historical and geographical region in southern Slovakia known for its mining heritage, medieval castles, and karst landscapes.
-
C.
Corfai
Corfai is a planet in the Star Wars universe located in the politically and economically important Core Worlds region.
-
D.
Grimus
Grimus is Salman Rushdie’s debut novel, a genre-blending work of science fiction and fantasy that explores themes of identity, immortality, and exile.
-
E.
Cagni
Cagni is an Italian surname most notably associated with Luigi Cagni, a former professional footballer and football manager.
- 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_69d8dd09d5a081909ae43c286651ae5a |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5f162ac648190a5f60c6a77b68304 |
completed | April 20, 2026, 9:26 a.m. |
Created at: April 10, 2026, 12:06 p.m.