Triple
T30692389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pitareti |
E781363
|
entity |
| Predicate | monasteryNearbyArchitectureStyle |
P171087
|
FINISHED |
| Object | Georgian medieval ecclesiastical architecture |
—
|
LITERAL 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: Georgian medieval ecclesiastical architecture | Statement: [Pitareti, monasteryNearbyArchitectureStyle, Georgian medieval ecclesiastical architecture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: monasteryNearbyArchitectureStyle Context triple: [Pitareti, monasteryNearbyArchitectureStyle, Georgian medieval ecclesiastical architecture]
-
A.
monasteryNearbyPeriod
Indicates that a monastery is located near a place or entity during a specified time period.
-
B.
monasteryType
Indicates the specific kind or classification of a monastery in relation to its broader religious or organizational category.
-
C.
hasNearbyMonastery
Indicates that one entity is located close to or in the vicinity of a monastery.
-
D.
containsMonastery
Indicates that one entity includes or encompasses a monastery within its boundaries or composition.
-
E.
monastery
Indicates that an entity is or functions as a monastery, typically a religious community or building where monastics live and practice.
- F. None of above. chosen
Provenance (4 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_69f224ab24e08190991d6edb6df58e8b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f698ad83a08190a6834056ccc3e3a4 |
completed | May 3, 2026, 12:37 a.m. |
| PD | Predicate disambiguation | batch_69f69664142c8190bc695501056b0236 |
completed | May 3, 2026, 12:27 a.m. |
| PDg | Predicate description generation | batch_69f697e92e2c8190bed50d5ba0981b64 |
completed | May 3, 2026, 12:33 a.m. |
Created at: April 29, 2026, 8:33 p.m.