Triple
T15258734
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kounistra Monastery |
E364715
|
entity |
| Predicate | locatedInForestSetting |
P39061
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Kounistra Monastery, locatedInForestSetting, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: locatedInForestSetting Context triple: [Kounistra Monastery, locatedInForestSetting, yes]
-
A.
locatedInForest
chosen
Indicates that an entity is situated within the boundaries of a forest.
-
B.
isForested
Indicates that an area or region is covered predominantly by forest or dense tree vegetation.
-
C.
locatedInParkLikeSetting
Indicates that something is situated within or directly surrounded by an environment resembling a park, typically featuring natural or landscaped outdoor elements.
-
D.
hasNearbyWoodland
Indicates that one entity is located close to or in the immediate vicinity of a woodland area associated with another entity.
-
E.
hasNearbyForestType
Indicates that one entity is located close to, or in the vicinity of, a forest of a specified type.
- F. None of above.
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_69d85a0f08408190b3c3259ae35d79d2 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0084d11148190919eef8e55569bb9 |
completed | April 15, 2026, 9:51 p.m. |
| PD | Predicate disambiguation | batch_69deca8d1bd48190a4b94f29b425e335 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:13 a.m.