Triple
T357443
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Walt Disney World Railroad |
E7574
|
entity |
| Predicate | usesFuel |
P1585
|
FINISHED |
| Object | diesel fuel (for firing boilers) |
—
|
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: diesel fuel (for firing boilers) | Statement: [Walt Disney World Railroad, usesFuel, diesel fuel (for firing boilers)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesFuel Context triple: [Walt Disney World Railroad, usesFuel, diesel fuel (for firing boilers)]
-
A.
fueledBy
chosen
Indicates that one entity provides the energy or power source that enables the operation or functioning of another entity.
-
B.
hasRefuellingCapabilityFor
Indicates that one entity is capable of providing or performing refuelling operations for another entity.
-
C.
fuelSystem
Indicates a relationship where one entity serves as the fuel system (or part of it) that supplies, stores, or manages fuel for the operation of another entity.
-
D.
usedOn
Indicates that one entity is applied to, operated on, or otherwise utilized in relation to another entity.
-
E.
drives
Indicates that one entity operates and controls the movement of a vehicle or similar conveyance transporting themselves or others.
- 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_69a2e7e696948190bebc966535995e45 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ebaf0c9881909313f98818e7fa58 |
completed | Feb. 28, 2026, 1:20 p.m. |
| PD | Predicate disambiguation | batch_69a2e959ce948190a201c017eecb7c95 |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.