Triple
T15514305
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boomerang (Worlds of Fun) |
E368793
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object | Vekoma |
E232240
|
NE 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: Vekoma | Statement: [Boomerang (Worlds of Fun), manufacturer, Vekoma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vekoma Context triple: [Boomerang (Worlds of Fun), manufacturer, Vekoma]
-
A.
Vekoma
chosen
Vekoma is a Dutch roller coaster and amusement ride manufacturer known worldwide for designing and building a wide range of thrill and family attractions for theme parks.
-
B.
Vaala
Vaala is a municipality in northern Finland known for its lakeside landscapes and location along the Oulujoki river.
-
C.
Veltro
Veltro is the nickname of the Macchi C.205, an Italian World War II fighter aircraft renowned for its speed and agility.
-
D.
Wokoma
Wokoma is a surname most notably associated with British actress and writer Susan Wokoma.
-
E.
Viddalba
Viddalba is a small town and comune in northern Sardinia, Italy, known for its rural setting and proximity to the Gallura region’s coastal and archaeological attractions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d85a1794cc8190b0b428716296e63e |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e04031e62c8190953b61207142af15 |
completed | April 16, 2026, 1:49 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff3d4edee481908382ca5cd266f7b0 |
completed | May 9, 2026, 1:57 p.m. |
Created at: April 10, 2026, 4:02 a.m.