Triple
T25207802
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southbank Theatre |
E631299
|
entity |
| Predicate | neighbourhoodLandmark |
P19575
|
FINISHED |
| Object | near Hamer Hall |
—
|
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: near Hamer Hall | Statement: [Southbank Theatre, neighbourhoodLandmark, near Hamer Hall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: neighbourhoodLandmark Context triple: [Southbank Theatre, neighbourhoodLandmark, near Hamer Hall]
-
A.
typicalNearbyLandmarks
Indicates that certain landmarks are commonly found in the vicinity of a given place or location.
-
B.
cityRegisteredLandmark
Indicates that a city has officially registered a particular landmark within its jurisdiction.
-
C.
notableNearbySite
chosen
Indicates that one entity is a significant or noteworthy site located close to another entity.
-
D.
cityLandmarkID
Indicates that a specific landmark is uniquely identified as being located within a particular city.
-
E.
southernNearbyLandmark
Indicates that one landmark is located to the south and in close proximity to another landmark.
- 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_69e75a8b86c4819089eda22c843b739f |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f474bda9288190947d325fe514571d |
completed | May 1, 2026, 9:39 a.m. |
| PD | Predicate disambiguation | batch_69f45cfb53f4819099bba48c5057e787 |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 21, 2026, 12:52 p.m.