Triple
T5503320
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gremi Church and Fortress |
E144377
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Gremi |
E144383
|
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: Gremi | Statement: [Gremi Church and Fortress, locatedIn, Gremi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gremi Context triple: [Gremi Church and Fortress, locatedIn, 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.
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.
-
D.
Gamelia
Gamelia is an epithet associated with the Greek goddess Hera, emphasizing her role as protector of marriage and weddings.
-
E.
Ganguise
Ganguise is a watercourse in southern France that feeds the artificial reservoir known as Lac de la Ganguise.
- 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_69c008f6b5048190a09064116062cf69 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c01f0bbea48190bb6fecaee9c0b1d0 |
completed | March 22, 2026, 4:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c027aa65608190a89fdfb0da675d4d |
completed | March 22, 2026, 5:32 p.m. |
Created at: March 22, 2026, 3:32 p.m.