Triple
T27883914
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalapaki Beach |
E705170
|
entity |
| Predicate | hasNearbyRestaurants |
P191961
|
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: [Kalapaki Beach, hasNearbyRestaurants, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyRestaurants Context triple: [Kalapaki Beach, hasNearbyRestaurants, yes]
-
A.
hasNearbyCommercialFacilities
chosen
Indicates that a place is located close to one or more commercial facilities, such as shops, restaurants, or other businesses.
-
B.
hasOutletNear
Indicates that one entity has a physical outlet or branch located in close proximity to another specified location or entity.
-
C.
hasNearbyFacility
Indicates that one entity is located close to or in the vicinity of a particular facility.
-
D.
servedNearbyEstates
Indicates that an entity provided services or assistance to estates located in its immediate geographic vicinity.
-
E.
nearbyVenue
Indicates that one venue is located close to another venue in physical space.
- 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_69ef96b39c448190a9b3aa6672a5168f |
completed | April 27, 2026, 5:02 p.m. |
| NER | Named-entity recognition | batch_69fd4129a8848190a5002150278ac689 |
completed | May 8, 2026, 1:49 a.m. |
| PD | Predicate disambiguation | batch_69fd3e0515ec8190937c7af71ebc3875 |
completed | May 8, 2026, 1:36 a.m. |
Created at: April 27, 2026, 6:31 p.m.