Triple
T19696453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Washington (Pittsburgh) |
E472971
|
entity |
| Predicate | isFamousForPhotography |
P9792
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Mount Washington (Pittsburgh), isFamousForPhotography, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isFamousForPhotography Context triple: [Mount Washington (Pittsburgh), isFamousForPhotography, true]
-
A.
notablePhotographer
Indicates that the subject is a photographer who is recognized as notable or significant in some context.
-
B.
usesPhotographyFrom
Indicates that one entity employs or incorporates photographic material originating from another entity.
-
C.
isPhotographicSubject
chosen
Indicates that an entity serves as the subject or main focus captured in a photograph taken by another entity.
-
D.
hasPhotographedFor
Indicates that one entity has taken photographs on behalf of, or as a service for, another entity.
-
E.
hasPhotographicSpecialty
Indicates that an entity possesses a specific area of expertise or focus within the field of photography.
- 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_69d8e515bef88190bc30781aea50537a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6421527b08190858788265043792d |
completed | April 20, 2026, 3:11 p.m. |
| PD | Predicate disambiguation | batch_69e53039ea808190a9106a53f564ab92 |
completed | April 19, 2026, 7:42 p.m. |
Created at: April 10, 2026, 1:46 p.m.