Triple
T23613539
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ben Saunders |
E583111
|
entity |
| Predicate | hasTattooStudio |
P83958
|
FINISHED |
| Object | tattoo shop in Hoorn, Netherlands |
—
|
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: tattoo shop in Hoorn, Netherlands | Statement: [Ben Saunders, hasTattooStudio, tattoo shop in Hoorn, Netherlands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTattooStudio Context triple: [Ben Saunders, hasTattooStudio, tattoo shop in Hoorn, Netherlands]
-
A.
hasTattoo
Indicates that one entity bears a tattoo on their body.
-
B.
hasNotableTattooArtist
Indicates that an entity is associated with a tattoo artist who is recognized as notable or distinguished.
-
C.
hasDesignStudio
Indicates that an entity owns, operates, or is associated with a design studio.
-
D.
hasStudio
Indicates that an entity (such as a film, game, or production) is associated with or produced by a particular studio.
-
E.
hasEstablishment
chosen
Indicates that one entity possesses, operates, or is associated with a particular establishment (such as a business, facility, or institution).
- 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_69e248fbcd9081908ba08913f9d30826 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b0f64c808190bf1cb1e4c916be17 |
completed | April 29, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69f118d0e0588190a86527a7747c5427 |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 17, 2026, 6:45 p.m.