Triple
T37131589
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kievskaya (Koltsevaya line) |
E919530
|
entity |
| Predicate | hasArtCategory |
P116586
|
FINISHED |
| Object | art in the Moscow Metro |
—
|
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: art in the Moscow Metro | Statement: [Kievskaya (Koltsevaya line), hasArtCategory, art in the Moscow Metro]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasArtCategory Context triple: [Kievskaya (Koltsevaya line), hasArtCategory, art in the Moscow Metro]
-
A.
artCategory
chosen
Indicates the classification relationship where an artwork is assigned to a particular artistic category or genre.
-
B.
hasArtSubjects
Indicates that an entity is associated with one or more subjects or themes within the domain of art.
-
C.
hasArtisticGenre
Indicates that an entity (such as a work or creation) belongs to or is characterized by a particular artistic genre.
-
D.
hasArtisticDiscipline
Indicates that one entity practices, specializes in, or is associated with a particular artistic discipline or field.
-
E.
hasArtFeature
Indicates that an entity possesses or is characterized by a particular artistic attribute, element, or stylistic feature.
- 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_69f76e9d13e48190a108f7fbf80ff375 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fe8f74748c8190bd14a856c057f9f7 |
completed | May 9, 2026, 1:35 a.m. |
| PD | Predicate disambiguation | batch_69fe8e7ed8088190929e0df67aca4de9 |
completed | May 9, 2026, 1:31 a.m. |
Created at: May 3, 2026, 4:15 p.m.