Triple
T31125273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York School of photography |
E793337
|
entity |
| Predicate | hasNotablePhotographer |
P19257
|
FINISHED |
| Object | Diane Arbus |
—
|
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: Diane Arbus | Statement: [New York School of photography, hasNotablePhotographer, Diane Arbus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotablePhotographer Context triple: [New York School of photography, hasNotablePhotographer, Diane Arbus]
-
A.
notablePhotographer
chosen
Indicates that the subject is a photographer who is recognized as notable or significant in some context.
-
B.
hasPhotographedFor
Indicates that one entity has taken photographs on behalf of, or as a service for, another entity.
-
C.
hasPhotographBy
Indicates that an entity is depicted in or associated with a photograph that was created or taken by a specified photographer.
-
D.
hasPhotographyValue
Indicates that something possesses a particular value, importance, or relevance specifically in the context of photography.
-
E.
hasPhotographAt
Indicates that a photograph depicting an entity was taken or exists at a specific location or event.
- 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_69f224d1701c819094f429798290e361 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_6a007ef69f5c8190be6fb89e918d30fa |
completed | May 10, 2026, 12:49 p.m. |
| PD | Predicate disambiguation | batch_6a007e4060448190ad7420b07c1fe219 |
completed | May 10, 2026, 12:46 p.m. |
Created at: April 29, 2026, 9:05 p.m.