Triple
T19630709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Diary of a Victorian Dandy |
E471260
|
entity |
| Predicate | languageOfVisualText |
P128895
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Diary of a Victorian Dandy, languageOfVisualText, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfVisualText Context triple: [Diary of a Victorian Dandy, languageOfVisualText, English]
-
A.
languageOfTextInImages
chosen
Indicates the language used in the textual content that appears within images.
-
B.
languageText
Indicates that a piece of text is expressed in, or associated with, a particular language.
-
C.
contentLanguage
Indicates the language in which the content is expressed or intended to be understood.
-
D.
languageOfKeyText
Indicates that a specified language is the primary language in which a given key or central text is written.
-
E.
languageView
Indicates a relationship where one entity views, interprets, or presents another entity through the lens of a particular language or linguistic perspective.
- 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_69d8e511f28481909f4bc3ea9191e54a |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e641025d708190aa44bb24671b9455 |
completed | April 20, 2026, 3:06 p.m. |
| PD | Predicate disambiguation | batch_69e514e5cb108190ae260e466c447314 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:44 p.m.