Triple
T23868764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alec Soth |
E592660
|
entity |
| Predicate | usesCameraFormat |
P58520
|
FINISHED |
| Object | large-format camera |
—
|
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: large-format camera | Statement: [Alec Soth, usesCameraFormat, large-format camera]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesCameraFormat Context triple: [Alec Soth, usesCameraFormat, large-format camera]
-
A.
usesCameraType
chosen
Indicates that one entity employs or operates a specific type or category of camera.
-
B.
sensorFormat
Indicates the data encoding or representation standard used by a sensor to output its measurements.
-
C.
usesFilmFormat
Indicates that one entity employs or is recorded in a particular film format associated with the other entity.
-
D.
usesCameraApp
Indicates that an entity operates or interacts with a camera application to capture photos, record videos, or manage camera-related functions.
-
E.
hasCameraResolution
Indicates that an entity is associated with a specific camera resolution value or specification.
- 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_69e25d23a5c88190ae3999c70ca15e08 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1cae643448190863c44df5f026482 |
completed | April 29, 2026, 9:09 a.m. |
| PD | Predicate disambiguation | batch_69f1614a65a88190bde1efb368a151e4 |
completed | April 29, 2026, 1:39 a.m. |
Created at: April 17, 2026, 8:14 p.m.