Triple
T19370149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St Hadrian civic guard company |
E484511
|
entity |
| Predicate | representedInGenre |
P33225
|
FINISHED |
| Object | schutterij portrait |
—
|
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: schutterij portrait | Statement: [St Hadrian civic guard company, representedInGenre, schutterij portrait]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: representedInGenre Context triple: [St Hadrian civic guard company, representedInGenre, schutterij portrait]
-
A.
coveredInGenre
chosen
Indicates that a work or item is associated with, categorized under, or treated within a particular genre.
-
B.
portraysCharacterInGenre
Indicates that an entity depicts or plays a character within works belonging to a specified genre.
-
C.
genreOfAppearance
Indicates the genre or type of creative work in which an entity appears.
-
D.
featuredInFilmGenre
Indicates that an entity (such as a film, character, or work) appears in or is associated with a specific film genre.
-
E.
hasGenreInRoles
Indicates that an entity participates in roles associated with a particular genre or set of genres.
- 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_69d8e8d305088190ad13571532aa454c |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e619af33e481908643f8beb2f498dc |
completed | April 20, 2026, 12:18 p.m. |
| PD | Predicate disambiguation | batch_69e4fd54f8e48190956e73dd8969164a |
completed | April 19, 2026, 4:05 p.m. |
Created at: April 10, 2026, 1:35 p.m.