Triple
T32324386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Girl with the Hatbox |
E825863
|
entity |
| Predicate | originalTitleLatin |
P9999
|
FINISHED |
| Object | Devushka s korobkoy |
—
|
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: Devushka s korobkoy | Statement: [The Girl with the Hatbox, originalTitleLatin, Devushka s korobkoy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalTitleLatin Context triple: [The Girl with the Hatbox, originalTitleLatin, Devushka s korobkoy]
-
A.
originalTitleName
Indicates that one entity is the original or primary title name associated with another entity.
-
B.
originalLanguageTitle
Indicates the title of a work as it appears in its original language of creation or publication.
-
C.
originalTitleLanguage
Indicates the language in which a work’s original title was written or expressed.
-
D.
hasLatinTitleOf
Indicates that one entity has, uses, or is associated with the Latin-language title corresponding to another entity.
-
E.
hasLatinTitle
chosen
Indicates that an entity possesses a title or name expressed in Latin.
- 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_69f34912d0c48190bba75770660320e9 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a018c6b7178819097d5450a1e3c2408 |
completed | May 11, 2026, 7:59 a.m. |
| PD | Predicate disambiguation | batch_6a018a4f741c8190babe721a908e2f5e |
completed | May 11, 2026, 7:50 a.m. |
Created at: May 1, 2026, 12:47 a.m.