Triple
T9010220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hang Gai Street |
E215447
|
entity |
| Predicate | hasPhotographicAppeal |
P57608
|
FINISHED |
| Object | street photography |
—
|
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: street photography | Statement: [Hang Gai Street, hasPhotographicAppeal, street photography]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhotographicAppeal Context triple: [Hang Gai Street, hasPhotographicAppeal, street photography]
-
A.
hasPhotographicSignificance
Indicates that something holds notable importance or relevance in the context of photography, such as for documentation, artistic value, or visual record.
-
B.
isPhotographicSubject
Indicates that an entity serves as the subject or main focus captured in a photograph taken by another entity.
-
C.
hasPhotograph
Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
-
D.
hasPhotoFeature
chosen
Indicates that an entity possesses a characteristic, capability, or option specifically related to photos or photography.
-
E.
hasPhotographicConvention
Indicates that there is an established photographic style, rule, or convention governing how the related entities are visually represented in photographs.
- 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_69ca83a2bf088190986ee7a8eb90407d |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69c00ae8819090786385a72e8baf |
completed | April 1, 2026, 12:41 a.m. |
| PD | Predicate disambiguation | batch_69cc5edf84408190aa5f57cb8bfd00e1 |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:06 p.m.