Triple
T18015433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MongoEngine |
E430986
|
entity |
| Predicate | usedWith |
P4791
|
FINISHED |
| Object | Tornado |
—
|
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: Tornado | Statement: [MongoEngine, usedWith, Tornado]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tornado Context triple: [MongoEngine, usedWith, Tornado]
-
A.
Tornado
Tornado is a steel inverted roller coaster located at Parque de Atracciones de Madrid in Spain.
-
B.
Tornado
Tornado is a high-thrill water slide attraction featuring steep drops and a large funnel element, located at the Wet'n'Wild Gold Coast water park in Australia.
-
C.
Tornado
Tornado is Zorro’s iconic black horse, known as his swift and loyal steed in the Zorro stories.
-
D.
Tornado
chosen
Tornado is a high-performance Python web framework and asynchronous networking library designed for handling large numbers of simultaneous connections.
-
E.
Tornado
The Tornado is a twin-engine, variable-sweep wing multirole combat aircraft developed jointly by the United Kingdom, Germany, and Italy, used for strike, reconnaissance, and electronic warfare missions.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.