Triple
T611691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bir-Hakeim |
E12112
|
entity |
| Predicate | hasPhotographicInterest |
P12849
|
FINISHED |
| Object | view of Eiffel Tower from viaduct |
—
|
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: view of Eiffel Tower from viaduct | Statement: [Bir-Hakeim, hasPhotographicInterest, view of Eiffel Tower from viaduct]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhotographicInterest Context triple: [Bir-Hakeim, hasPhotographicInterest, view of Eiffel Tower from viaduct]
-
A.
isPhotographicSubject
Indicates that an entity serves as the subject or main focus captured in a photograph taken by another entity.
-
B.
hasPhotograph
chosen
Indicates that one entity possesses, includes, or is associated with a photograph depicting or representing another entity.
-
C.
hasAperture
Indicates that one entity possesses or is characterized by a specific opening, gap, or aperture.
-
D.
hasGallery
Indicates that one entity possesses, contains, or is associated with a gallery, such as a collection or display space.
-
E.
photographer
Indicates that one entity takes photographs of another entity, typically in a professional or intentional capacity.
- 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_69a493309df48190a327f748e88049a6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49e07739481909930a6577c081b9e |
completed | March 1, 2026, 8:13 p.m. |
| PD | Predicate disambiguation | batch_69a49cfa7b4481909bec7a5fd3e98c65 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.