Triple
T5853157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beech Bend Park |
E130084
|
entity |
| Predicate | hasRollerCoaster |
P23566
|
FINISHED |
| Object | Zero G |
E550202
|
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: Zero G | Statement: [Beech Bend Park, hasRollerCoaster, Zero G]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zero G Context triple: [Beech Bend Park, hasRollerCoaster, Zero G]
-
A.
Zero G
chosen
Zero G is a roller coaster attraction located at Beech Bend Park in Kentucky.
-
B.
Zero K
Zero K is a 2016 novel by Don DeLillo that explores themes of mortality, technology, and cryonics through a meditative, dystopian narrative.
-
C.
Le Shuttle
Le Shuttle is a vehicle-carrying rail service that transports cars, trucks, and passengers through the Channel Tunnel between the United Kingdom and France.
-
D.
G-Force
G-Force is a 2009 live-action/CGI family action-comedy film about a team of highly trained secret agent guinea pigs, produced by Jerry Bruckheimer.
-
E.
Mission: SPACE
Mission: SPACE is a spaceflight-simulation attraction at EPCOT that immerses guests in a realistic, high-intensity mission to Mars experience.
- 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_69c0084de39081909eb34e6bed74215a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c035529cf88190acc547ae839950e7 |
completed | March 22, 2026, 6:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0b1007bbc81908963d3d14c9210c5 |
completed | March 23, 2026, 3:18 a.m. |
Created at: March 22, 2026, 3:55 p.m.