Triple
T6095498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sceaux, France |
E135866
|
entity |
| Predicate | hasParkStyle |
P68091
|
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: [Sceaux, France, hasParkStyle, French formal garden]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParkStyle Context triple: [Sceaux, France, hasParkStyle, French formal garden]
-
A.
hasParkFeature
Indicates that a park includes, contains, or is associated with a specific feature or amenity.
-
B.
hasParkSection
Indicates that one entity includes, contains, or is associated with a specific section or area of a park.
-
C.
hasParkStatus
Indicates that an entity holds a particular designation or status related to being a park (e.g., national park, city park, protected parkland).
-
D.
hasParkAccess
Indicates that an entity is permitted to enter, use, or otherwise access a specified park or park area.
-
E.
hasParking
Indicates that a place or facility provides designated parking space(s) available for use.
- 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_69c0087cd3c48190b459848c72d84eb1 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05a963bac8190bc0c33fef187875c |
completed | March 22, 2026, 9:09 p.m. |
| PD | Predicate disambiguation | batch_69c049f5ac988190b62ba565153aaa35 |
completed | March 22, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69c04e8e3f2c8190be459ca02f9b315a |
completed | March 22, 2026, 8:18 p.m. |
Created at: March 22, 2026, 4:12 p.m.