Triple
T16757760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donegall Place |
E407252
|
entity |
| Predicate | proximityToLandmarks |
P53174
|
FINISHED |
| Object | close to key city landmarks in Belfast |
—
|
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: close to key city landmarks in Belfast | Statement: [Donegall Place, proximityToLandmarks, close to key city landmarks in Belfast]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proximityToLandmarks Context triple: [Donegall Place, proximityToLandmarks, close to key city landmarks in Belfast]
-
A.
proximityToLandmark
chosen
Indicates a spatial relationship where one entity is located near or close to a specified landmark.
-
B.
typicalNearbyLandmarks
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
-
C.
nearbyTo
Indicates that one entity is located close in distance or position to another entity.
-
D.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
-
E.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
- 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_69d8839174188190909f190097207065 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3abea156c8190a5ee6d0028471810 |
completed | April 18, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69e319cbd79c8190a03587a61c18bec0 |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:21 a.m.