Triple
T617800
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Herrenhausen Palace |
E14443
|
entity |
| Predicate | hasGardenStyle |
P12538
|
FINISHED |
| Object | French formal garden |
—
|
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: French formal garden | Statement: [Herrenhausen Palace, hasGardenStyle, French formal garden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGardenStyle Context triple: [Herrenhausen Palace, hasGardenStyle, French formal garden]
-
A.
hasGardenType
chosen
Indicates that an entity possesses or is associated with a garden of a specified type.
-
B.
hasLandscaping
Indicates that an entity possesses or is associated with designed outdoor grounds or landscape features.
-
C.
hasCourtyard
Indicates that one entity includes, features, or is characterized by the presence of a courtyard.
-
D.
hasBotanicalGarden
Indicates that one entity possesses, contains, or includes a botanical garden as part of its facilities or domain.
-
E.
plantingStyle
Indicates the method or arrangement used to plant entities in relation to each other or their environment.
- 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_69a4934b17c881909ace8270e8ddd202 |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e2418c881908552d2c4a5006e97 |
completed | March 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69a49cfd15288190b4abdbd0bce3edcd |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.