Triple
T24222390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Girl with a Leica |
E601494
|
entity |
| Predicate | hasCameraBrandDepicted |
P40833
|
FINISHED |
| Object | Leica |
—
|
NE NERFINISHED |
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: Leica | Statement: [Girl with a Leica, hasCameraBrandDepicted, Leica]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCameraBrandDepicted Context triple: [Girl with a Leica, hasCameraBrandDepicted, Leica]
-
A.
cameraBranding
chosen
Indicates that one entity serves as the brand or branding designation associated with a camera or camera product.
-
B.
hasCamera
Indicates that an entity is equipped with or possesses a camera.
-
C.
hasPhotogenicFeature
Indicates that an entity possesses a visual characteristic or attribute that is especially attractive or appealing when photographed.
-
D.
hasPhotoFeature
Indicates that an entity possesses a characteristic, capability, or option specifically related to photos or photography.
-
E.
usesCameraType
Indicates that one entity employs or operates a specific type or category of camera.
- 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_69e29537ca548190b94a37ebe1977caf |
completed | April 17, 2026, 8:16 p.m. |
| NER | Named-entity recognition | batch_69f287dc0b388190bcbbf3f7c61e421d |
completed | April 29, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69f1c448abec8190b87cbf9ed419a309 |
completed | April 29, 2026, 8:41 a.m. |
Created at: April 18, 2026, midnight