Triple
T26268217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alys Tomlinson |
E657046
|
entity |
| Predicate | hasPhotographyTechnique |
P54389
|
FINISHED |
| Object | large-format film |
—
|
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 film | Statement: [Alys Tomlinson, hasPhotographyTechnique, large-format film]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPhotographyTechnique Context triple: [Alys Tomlinson, hasPhotographyTechnique, large-format film]
-
A.
hasPhotographicSpecialty
Indicates that an entity possesses a specific area of expertise or focus within the field of photography.
-
B.
hasPhotogenicFeature
Indicates that an entity possesses a visual characteristic or attribute that is especially attractive or appealing when photographed.
-
C.
hasPhotographicProcess
chosen
Indicates that something is associated with, created by, or characterized through a specific photographic process or technique.
-
D.
hasPhotographicActivity
Indicates that one entity engages in or is involved with photographic activity in relation to another entity or context.
-
E.
usesPhotographyFrom
Indicates that one entity employs or incorporates photographic material originating from another entity.
- 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_69ee5b4e21bc819082be98bc9ab09796 |
completed | April 26, 2026, 6:37 p.m. |
| NER | Named-entity recognition | batch_69f61f12b0f08190bc4a16907941864c |
completed | May 2, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69f61b3a8ae0819090189fbd8eb19f2f |
completed | May 2, 2026, 3:41 p.m. |
Created at: April 26, 2026, 9:12 p.m.