Triple
T15200985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Freycinet gauge |
E363265
|
entity |
| Predicate | typicalCargoCapacity_tonnes |
P46451
|
FINISHED |
| Object | 250 |
—
|
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: 250 | Statement: [Freycinet gauge, typicalCargoCapacity_tonnes, 250]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalCargoCapacity_tonnes Context triple: [Freycinet gauge, typicalCargoCapacity_tonnes, 250]
-
A.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
-
B.
estimatedMassInTonnes
Indicates the approximate mass of an entity expressed in metric tonnes.
-
C.
typicalReturnCargo
Indicates that something is the kind of cargo that is usually carried back on a return trip or journey.
-
D.
cargoCapacityFeature
chosen
Indicates that an entity has a feature specifying how much cargo it can carry or accommodate.
-
E.
tonnageClass
Indicates a classification relationship where an entity is assigned to a category based on its tonnage (weight or carrying capacity range).
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006b588b88190a88e91d521acbdfe |
completed | April 15, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69deb97ee9d881908711dbe12a55283c |
completed | April 14, 2026, 10:02 p.m. |
Created at: April 10, 2026, 3:10 a.m.