Triple
T5503311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | David Gareja monastery complex |
E144376
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Sagarejo |
E159286
|
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: Sagarejo | Statement: [David Gareja monastery complex, nearCity, Sagarejo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sagarejo Context triple: [David Gareja monastery complex, nearCity, Sagarejo]
-
A.
Sagarejo
chosen
Sagarejo is a town in eastern Georgia that serves as an important local center in the Kakheti wine-producing region.
-
B.
Ravanica
Ravanica is a river in central Serbia that flows through the region before joining the Velika Morava.
-
C.
Morača
Morača is a major river in Montenegro that flows through the capital city of Podgorica before emptying into Lake Skadar.
-
D.
Ilijaš
Ilijaš is a town and municipality in central Bosnia and Herzegovina, situated northwest of Sarajevo and known for its industrial heritage and surrounding hilly landscape.
-
E.
Baška
Baška is a popular coastal town and tourist resort on the island of Krk in Croatia, known for its long pebble beach and historic old town.
- 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_69c008f5a2748190bce7a39aabf87a6d |
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.