Triple
T20188174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Intaride LLC |
E492915
|
entity |
| Predicate | hasParentBrand |
P6092
|
FINISHED |
| Object | Intamin |
—
|
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: Intamin | Statement: [Intaride LLC, hasParentBrand, Intamin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Intamin Context triple: [Intaride LLC, hasParentBrand, Intamin]
-
A.
Intamin
chosen
Intamin is a Swiss-based company renowned worldwide for designing and manufacturing major amusement rides and roller coasters for theme parks.
-
B.
Tokyo Denki
Tokyo Denki was a Japanese electrical company that became a predecessor of the modern electronics conglomerate Toshiba.
-
C.
Tokyu Land Corporation
Tokyu Land Corporation is a major Japanese real estate developer and property management company within the Tokyu Group conglomerate.
-
D.
Kawada Industries
Kawada Industries is a Japanese engineering and construction company known for its work on major infrastructure projects, including prominent bridges and civil works.
-
E.
Nimco
Nimco is the wife of Somali-born British journalist and television news presenter Rageh Omaar.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66ad2c43c8190a2fc5ef2a0514e53 |
completed | April 20, 2026, 6:05 p.m. |
Created at: April 11, 2026, 11:37 p.m.