Triple
T1053332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Boeing 747 |
E22746
|
entity |
| Predicate | typicalTwoClassCapacity |
P1931
|
FINISHED |
| Object | around 400 passengers |
—
|
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: around 400 passengers | Statement: [Boeing 747, typicalTwoClassCapacity, around 400 passengers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTwoClassCapacity Context triple: [Boeing 747, typicalTwoClassCapacity, around 400 passengers]
-
A.
typicalCapacity
chosen
Indicates the usual or standard amount, volume, or capability that something is designed or expected to hold, handle, or perform under normal conditions.
-
B.
totalCapacity
Indicates the maximum amount or volume that something can hold or accommodate in total.
-
C.
approximateCapacity
Indicates that one entity has an estimated or rough capacity value relative to another or to a specified measure.
-
D.
maximumCapacity
Indicates the greatest allowable or designed amount of something that an entity can hold, contain, or handle.
-
E.
typicalUnitSize
Indicates the standard or most common size or quantity in which something is typically measured, packaged, or used.
- 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_69a493da02e081908c13ff5e02a0fe7a |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4b8b644088190a1f0f00f97941298 |
completed | March 1, 2026, 10:07 p.m. |
| PD | Predicate disambiguation | batch_69a4b731e25c8190b5ea8466648c2c9a |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.