Triple
T6682947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ophir Hall |
E152030
|
entity |
| Predicate | hasParkLikeGrounds |
P45219
|
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: [Ophir Hall, hasParkLikeGrounds, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasParkLikeGrounds Context triple: [Ophir Hall, hasParkLikeGrounds, yes]
-
A.
hasParks
Indicates that one entity possesses, contains, or is associated with one or more parks.
-
B.
hasSportsGround
Indicates that an entity possesses, includes, or is associated with a sports ground or athletic field as part of its facilities or area.
-
C.
hasParkArea
Indicates that an entity includes or is associated with a designated park or recreational area within its boundaries.
-
D.
hasGreenSpaces
chosen
Indicates that an entity includes or is associated with areas of vegetation or natural greenery, such as parks, gardens, or lawns.
-
E.
hasParkAndGardenGrade
Indicates that an entity has been assigned a specific quality or rating level for its parks and gardens.
- 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_69c687f9977c819097e7f5ada4fe522e |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6c0aa8c5c8190a302b261f11b70cb |
completed | March 27, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c6ad0b6d00819086205b8ce30dd045 |
completed | March 27, 2026, 4:15 p.m. |
Created at: March 27, 2026, 2:04 p.m.