Triple
T9978836
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Botanical Garden Munich-Nymphenburg |
E196398
|
entity |
| Predicate | hasGreenhousesCount |
P91406
|
FINISHED |
| Object | multiple |
—
|
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: multiple | Statement: [Botanical Garden Munich-Nymphenburg, hasGreenhousesCount, multiple]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGreenhousesCount Context triple: [Botanical Garden Munich-Nymphenburg, hasGreenhousesCount, multiple]
-
A.
hasGreenhouse
Indicates that an entity possesses or includes a greenhouse structure or facility.
-
B.
numberOfGardens
Indicates the quantity of gardens associated with a given entity.
-
C.
numberOfPlants
Indicates the total count of plants associated with a given entity or context.
-
D.
hasGreenSpaces
Indicates that an entity includes or is associated with areas of vegetation or natural greenery, such as parks, gardens, or lawns.
-
E.
containsGarden
Indicates that one entity includes or has a garden within its area or boundaries.
- 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_69ca82efbce081908179b4b9c65096eb |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdb850bde48190a06b77757f8c081b |
completed | April 2, 2026, 12:29 a.m. |
| PD | Predicate disambiguation | batch_69cd1d9daa808190b413a1b9a1e929e2 |
completed | April 1, 2026, 1:29 p.m. |
| PDg | Predicate description generation | batch_69cd358386f48190833c862b5b8c04b2 |
completed | April 1, 2026, 3:10 p.m. |
Created at: March 30, 2026, 8:49 p.m.