Triple
T3334406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Botanischer Garten der Universität Leipzig |
E70105
|
entity |
| Predicate | hasGreenhouse |
P47974
|
FINISHED |
| Object | tropical greenhouse |
—
|
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: tropical greenhouse | Statement: [Botanischer Garten der Universität Leipzig, hasGreenhouse, tropical greenhouse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGreenhouse Context triple: [Botanischer Garten der Universität Leipzig, hasGreenhouse, tropical greenhouse]
-
A.
hasGreenhouseEffect
Indicates that one entity causes or contributes to a greenhouse effect on another entity, typically by trapping heat through atmospheric or environmental mechanisms.
-
B.
hasGreenSpaces
Indicates that an entity includes or is associated with areas of vegetation or natural greenery, such as parks, gardens, or lawns.
-
C.
hasVillageGreen
Indicates that one entity possesses or includes a village green as part of its area or facilities.
-
D.
containsGarden
Indicates that one entity includes or has a garden within its area or boundaries.
-
E.
hasGreenType
Indicates that an entity possesses or is associated with a type classified as green.
- 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_69ad85a24f208190bcf83131bfed3521 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb1961b888190bda38ba301ddc51d |
completed | March 8, 2026, 5:27 p.m. |
| PD | Predicate disambiguation | batch_69ada42c2ba8819091136805ce17b39d |
completed | March 8, 2026, 4:30 p.m. |
| PDg | Predicate description generation | batch_69adaa518ac88190b64f949ace018ab7 |
completed | March 8, 2026, 4:56 p.m. |
Created at: March 8, 2026, 3:12 p.m.